VictorJuiz/thesis huggingface dataset:
Trauma-Informed Practices for Teachers of Refugee Students

April Ball centered her capstone project on creating a compilation of the available literature on trauma informed care for immigrant and refugee populations. This included an analysis of the impact of trauma on children and a practical list of tangible skills and practices teachers can employ when working with traumatized children. Informed by her time as a student in HUMRTS 108- Advanced Spanish Service-Learning: Migration, Asylum, and Human Rights at the Border, April recognizes how trauma can detrimentally affect people’s lives if not managed correctly and staunchly believes mental healthcare is not only a human right, but necessary and essential in the recovery and integration process for refugee communities. With this research, April hopes to contribute to the expanding field of trauma informed care and hopes to contribute to a cohesive understanding of the intersection between immigrant rights and mental health.

Keywords: Psychic trauma in children
Immigrants
Refugee children
Mental health services
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Thesis
Treatment-induced antigen presentation in cancer

Determination of self versus non-self is vital to the health of all organisms, and is in part determined by Major Histocompatibility Complex (MHC) proteins in mammals. While evolved to protect against pathogens, MHC molecules may also present tumor-associated antigens to cell surfaces for T-cell surveillance. Presentation of tumor-associated antigens on MHC is vital for efficacy of most currently available immunotherapy regimens. Therefore, cancer immunotherapy treatments such as Immune Checkpoint Blockade are ineffective in malignancies that have mid- to low- Tumor Mutation Burden (TMB). This work outlines the effect of three treatments on various human cancer cell lines. The first study determines the effect of mTOR inhibitor rapamycin on Burkitt Lymphoma (Raji) cells and concludes that rapamycin upregulates the expression of several MHC molecules, leading to the presentation of more unique peptides. The second study analyzes the effect of interferon-gamma, a cytokine that upregulates the expression of MHC molecules as well as other components of the antigen processing and presentation (APP) pathway. Our results indicate that interferon-gamma increases the proteome coverage of MHC-associated peptides due to these effects on APP proteins. The final study in this work elucidates the effect of gamma-irradiation on the head and neck squamous cell carcinoma (HNSCC) cell line, PCI-13, in a 3D cell-derived xenograft model. The results of gamma-irradiation were surprising: irradiated tumors had markedly fewer MHC-associated peptides than matched controls. In conclusion, exogenous perturbations can have dramatic effects on antigen presentation and should be studied further in order to better understand how cancer therapeutics affect antigen presentation, and therefore immunotherapy efficacy

Keywords: Chemical and Systems Biology.
Advisor: Elias Joshua
Wandless Thomas
Grimes Kevin
Trends in COVID-19 transmission across United States Immigrations and Customs Enforcement facilities and the effectiveness of prevention strategies, 2020-2022

Background: The Covid-19 pandemic has significantly increased the health risks posed to incarcerated populations, including individuals detained by the United States Immigration and Customs Enforcement agency (ICE). Despite the enactment of preventative strategies (Pandemic Response Requirement; PRR), it is unclear whether these policies have reduced Covid-19 transmission in ICE facilities. In this study, we describe trends in Covid-19 cases across ICE facilities and model the effects of two significant PRR policies (V5.0 and V6.0) on viral transmission to inform future infection prevention strategies.
Methodology: We estimated trends for a series of descriptive measures of Covid-19 transmission, including point prevalence, across the ICE detention system from March 2020 through January 2022. After identifying facilities with complete datasets, we estimated the effective reproductive number of Covid-19 (Rt) per facility. Finally, we used an interventional time series model to estimate the effects of two significant PRR policies on facility effective reproductive numbers and performed sensitivity analyses.
Findings: Across the ICE system, 37,720 cumulative cases of Covid-19 were reported through January 2022 with a median point prevalence of 3.295% (IQR:2.472 - 4.309%). Across the facilities included in the model (N=93 of 146), the median population-weighted effective reproductive number was Rt=1.279 (IQR: 1.091 – 1.532). PRR policies V5.0 and V6.0 were not associated with immediate effects (V5.0: -0.196, 95%CI of -0.591 – 0.199; V6.0: -0.076, 95%CI of -0.304 – 0.152) or sustained effects (V5.0: <0.001, 95%CI of -0.001 – 0.002; V6.0: 0.001, 95%CI of <0.001 – 0.002) on facility effective reproductive numbers. The effects of PRR policies were sensitive to the variations in the delay from policy enactment to implementation.
Interpretation: Revisions to ICE PRR policies to improve testing, screening, and vaccination strategies did not reduce the transmission of Covid-19 within detention facilities with peak transmission found months after the changes were enacted. We recommend a comprehensive evaluation of the effectiveness of current public health policies utilized in detention facilities and the development of novel strategies to reduce infection risk.

Keywords: Lo, Nathan
Ulrich, Angela
COVID-19/epidemiology, COVID-19/statistics and numerical data, United States Government Agencies, Jails, Policy/trends, Interrupted Time Series Analysis
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Thesis
Advisor: Maldonado Yvonne
Winslow Dean
Truncation algorithms for Markov chains and processes

A well known approach for approximating the stationary distribution of a very large or infinite state space Markov chain is to take a finite sub-matrix of the original infinite transition kernel, augment the entries of the sub-matrix to produce a stochastic matrix, and use the stationary distribution of the resulting stochastic matrix as the approximation. This algorithm is known as truncation and augmentation. We present new sufficient conditions for establishing the convergence of general truncation and augmentation schemes. In particular, we show that convergence is guaranteed when the truncation sets are chosen as sublevel sets of a certain Lyapunov function. We also give generalizations of these results to the continuous state space and continuous time settings. These new sufficient conditions prove useful: for example, they give to our knowledge the first proof of convergence of general augmentations for the toggle switch model in biology. We also present generalizations of known convergence results on "fixed state" augmentations, wherein the augmentation in truncation and augmentation is only performed on a single column, to continuous state space and continuous time. We observe that the probabilistic concept of regeneration can play a key role in establishing convergence of these schemes, via a suitable probabilistic coupling and a monotone convergence argument. Finally, we present a new algorithm for approximating the stationary distribution of a very large or infinite state space Markov chain using a smaller state space. The algorithm is based on a ratio representation for equilibrium expectations in which the numerator and denominator correspond to expectations defined over paths that start and end within a given return set K. When K is a singleton, this representation is a well known consequence of regenerative process theory. For computational tractability, we ignore contributions to the path expectations corresponding to excursions out of a given truncation set A. This yields a truncation algorithm that is provably convergent as A gets large. Furthermore, in the presence of a suitable Lyapunov function, we can bound the path expectations, thereby providing computable and convergent error bounds for our numerical procedure. We also present a computational study which compares our algorithm to other algorithms in the literature and shows that our method achieves state of the art performance on models from biology and queueing

Advisor: Glynn Peter W.
Blanchet Jose H.
Iaccarino Gianluca
Trustworthy machine learning by efficiently verifying compressed models

Autonomous decision-making systems are becoming ever more pervasive, and we are increasingly relying on these systems to perform actions for us. Previously, we mostly used algorithms for simple predictive tasks. Currently, we encounter them navigating sequential decision-making scenarios where they are crafted for choosing the sequence of actions that lead to, ideally, the maximal expected performance. With the widespread availability of data, increase in computational power, and advances in learning algorithms, machine learning is becoming a viable alternative to traditionally expert-crafted solutions. Machines are able to learn from data and build representations of the world that guide their actions. Recently, artificial neural networks have become very popular function approximators. Many astounding achievements of computer intelligence, from automated language translation to self-driving cars, are based on neural networks. In particular, their combination with Reinforcement Learning (RL) has enabled machines to learn the solution to complex sequential problems. Unlike traditional software, it is nearly impossible for a human to understand the logic that neural networks implements, making them opaque models and potentially precluding their use in safety or mission-critical applications. In many settings, running simulations is not enough to build confidence in them because one single failure can lead to catastrophic consequences. The work in this thesis addresses the challenge of building trust in machine-learned systems with neural network components. We begin by introducing neural network verification, a procedure that certifies whether a network upholds a desired property or not. We present recent advances to neural network verification, including our own contributions, and show that, despite progress, verification continues to be a very challenging problem and current algorithms struggle to scale to large networks. We then present an alternative approach that incorporates the need to verify into the design of the model. Simpler models are easier to verify and we show that some problems can be solved with binarized neural networks (BNNs), significantly simpler models with parameters that can be represented with 1 bit, with similar performance to their full precision counter-parts. We propose and demonstrate a simple mixed-integer programming approach to verify them and show that this approach scales well. Finally, we present a deep reinforcement learning algorithm, similar to Deep Q-Learning that uses BNNs as function approximators and we show that, again, this approach is able to tradeoff a small amount of performance and gain scalable verification

Advisor: Kochenderfer Mykel J.
Boyd Stephen P. 
Pilanci Mert
Tuning optical properties of 2D semiconductors by strain and electrostatic gating

Due to their two-dimensional (2D) nature, atomically thin transition metal dichalcogenides (TMDCs) exhibit properties that are greatly distinct from their bulk counterparts. At the single layer limit, the strong light-matter interaction and transition from indirect-gap to direct-gap enable new fundamental physics and optoelectronic applications. It is of great significance that the optical properties of atomically thin TMDCs could be controlled by external tuning knobs. In the dissertation, two external knobs are presented: strain and electrostatic gating. For strain tuning, we use bendable flexible substrates to apply uniaxial tensile strain to monolayer WSe2 samples and probe them with absorption and PL measurements. We show that tensile strain around 2.1% not only reduces the optical band gap by around 100 meV, but also greatly narrows the exciton linewidth. By modeling different components to excitonic linewidth, we attribute this reduction to the suppressed intervalley exciton-phonon scattering under tensile strain. This explanation is further consolidated by measurements on monolayer WS2 and bilayer WSe2. Another external tuning knob investigated is the presence of free charge carriers in the system, which could be injected into 2D materials by electrostatic gating. A high carrier concentration (~ 1013 cm-2) is realized through the electric-double-layer gating technique with ionic solid LaF3. Doping-dependent absorption measurements on monolayer WSe2 show that excitons are dissociated due to increased Coulomb screening at high free carrier concentrations. As a result, excitonic peaks are gradually replaced by a step-function like band-to-band transition feature in absorption spectra. Distinctive effects of screening and state filling due to free carriers are also observed through the different behaviors of A/B excitonic features. The works presented in this dissertation demonstrate that strain and electrostatic gating are two powerful knobs for both probing the fundamental physics in 2D semiconductors and realizing novel optoelectronic applications

Keywords: Applied Physics.
Advisor: Heinz Tony F.
Lindenberg Aaron Michael
Pop Eric
Two languages, Many Stages: Bartolomé de Alva’s Nahuatl Theatrics in Seventeenth Century Nueva España

In the seventeenth century in Nueva España, Bartolomé de Alva, a multilingual Jesuit priest of Spanish and noble Texcoco descent, produced three works: a Spanish-Nahuatl guide to Confession, three translations of Spanish Golden Age plays into Nahuatl, and an introduction to Horacio Carochi’s Nahuatl grammar and dictionary. Throughout all three of these works, despite their differing genres, I identify theatricality. In the Confessionario I see this in the script and stage directions Alva gives to the characters he constructs. Theatricality is intentionally avoided in Alva’s Nahuatl translation of meta-theater in Pedro Calderón de la Barca’s original “El gran teatro del mundo.” Lastly, the liberties that Alva takes with the introduction to the dictionary defy the bureaucratic genre and replace it with his own personal monologue. In each of these objects, Alva uses or avoids theatricality alongside Nahuatl to mediate and soften differences between the Nahua life and the colonial vision of Christian conversion and assimilation. Yet, use of these languages is based in the faulty notion that Spanish and Nahuatl provide two sets of words that can be exchanged and still inform the same worlds. Ultimately, these three translation projects reveal the Nahuatl language’s incongruity with the goals of Alva’s religious and epistemic colonization, which only exist in his theater.

Keywords: Nahuatl language
Nahuatl drama
Nahuatl language > Study and teaching > Spanish speakers
Conversion > Christianity
New Spain
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Thesis
Advisor: Hughes Nicole T.
Uncertainty and information for machine learning powered decision-making

Machine learning (ML) is undergoing a paradigm shift --- ML models are increasingly being provided as a service to automate a variety of downstream decisions rather than being trained and deployed end-to-end on specific tasks by ML experts. Examples include image or text classification APIs provided by large tech companies and used by a wide range of third-party app developers, and various forecasts (e.g., weather, COVID, traffic, etc.) provided through websites to millions of users to help with their planning. Although this new paradigm democratizes ML by making it more widely accessible, it raises concerns about trustworthiness (users have no visibility on how they were trained and their failure modes) and performance (prediction models are no longer tailored for a specific downstream task). This dissertation tackles these issues by: Contribution 1. proposing a new approach to convey confidence to downstream decision makers who will use the predictions for (high stakes) decisions by accurate uncertainty quantification. Accurate uncertainty quantification can be achieved by predicting the true probability of the outcome of interest (such as the true probability of a patient's illness given the symptoms). While outputting these probabilities exactly is impossible in most cases, I show that it is surprisingly possible to learn probabilities that are indistinguishable from the true probabilities for large classes of decision making tasks. Indistinguishability ensures reliability for decision makers, because they should not be able to tell the difference between the predicted probability and the true probability in their decision tasks. As an application, I develop predictions models in domains such as medical diagnosis, flight delay prediction, and poverty prediction. I show that by using my methods, decision makers can confidently make decisions leading good outcomes. % where the Contribution 2. developing a new theory of information to rigorously reason about and optimize the ``usefulness'' of ML predictions in a wide range of decision tasks. Shannon information theory has wide applications in machine learning, but suffers several limitations when applied to complex learning and decision tasks. For example, consider a dataset of securely encrypted messages intercepted from an opponent. According to information theory, these encrypted messages have high mutual information with the opponent's plans, yet any computationally bounded decision maker cannot utilize this information. To address these limitations, I put forward a new framework called ``utilitarian information theory'' that generalizes Shannon's entropy, information and divergence to account for how information will be used by a decision maker with limited knowledge or modeling power. As an application, I apply the new information to Bayesian optimization problems and show an order of magnitude improvements in sample efficiency compared to current methods that use Shannon information

Advisor: Ermon Stefano
Finn Chelsea
Ma Tengyu
Uncovering host responses to malaria infection in the hematopoietic niche

Intensive malaria threatens maternal and child health among the world's most impoverished populations in the tropical and subtropical zone. Malaria is a mosquito-borne disease caused by apicomplexan parasites of the genus Plasmodium. Profound anemia is a frequent complication of severe malaria in young children living in areas of high transmission. Severe malarial anemia results from infection and destruction of RBCs in circulation and inadequate replenishment with fresh RBCs differentiated from erythroid stem cells in the bone marrow (erythropoiesis). Parasites are known to localize to the bone marrow which recent work has highlighted as a site of gametocytogenesis. How interactions between host and parasite in the bone marrow contribute to altered red cell production (dyserythropoiesis) in malaria infection remains a crucial missing piece in the current understanding of malarial pathogenesis. The research described in this dissertation addresses a substantial gap in knowledge about host-parasite interactions in the hematopoietic niche that may contribute to dyserythropoiesis. Here, an ex vivo system of erythroid differentiation from primary human bone marrow stem cells is used to investigate the stage-specific responses of developing erythroblasts to P. falciparum. Transcriptomic and proteomic approaches to analyzing infected erythroblasts at various stages of development reveal substantial disruption to programs of erythroid gene expression and induction of proteotoxic stress in response to P. falciparum. This work opens new avenues for investigating an understudied site of malaria pathogenesis and a major niche for cellular development of both host and parasite

Keywords: Microbiology and Immunology.
Advisor: Egan Elizabeth S.
Boothroyd John C.
Schneider David (David Samuel)
Understanding and learning robotic manipulation skills from humans

Humans are constantly learning new skills and improving upon their existing abilities. In particular, when it comes to manipulating objects, humans are extremely effective at generalizing to new scenarios and using physical compliance to our advantage. Compliance is key to generating robust behaviors by reducing the need to rely on precise trajectories. Inspired by humans, we propose to program robots at a higher level of abstraction by using primitives that leverage contact information and compliant strategies. Compliance increases robustness to uncertainty in the environment and primitives provide us with atomic actions that can be reused to avoid coding new tasks from scratch. We have developed a framework that allows us to: (i) collect and segment human data from multiple contact-rich tasks through direct or haptic demonstrations, (ii) analyze this data and extract the human's compliant strategy, and (iii) encode the strategy into robot primitives using task-level controllers. During autonomous task execution, haptic interfaces enable human real-time intervention and additional data collection for recovery from failures. The framework was extensively validated through simulation and hardware experiments, including five real-world construction tasks

Keywords: Mechanical Engineering.
Advisor: Khatib Oussama
Okamura Allison
Bohg Jeannette
Understanding feature use divergences between human and machine vision

Recent work has highlighted a seemingly sharp divergence between human and machine vision: studies have argued that, whereas people exhibit a shape bias, preferring to classify objects according to their shape (Landau et al., 1988; Geirhos et al., 2018; Kucker et al., 2019), standard ImageNet-trained CNNs privilege texture (Geirhos et al., 2018). How prevalent is this texture bias, and where does it come from? I will present evidence that, while both model architecture and training objective affect a model's level of texture bias, the statistics of the training data are the most important factor, and that naturalistic data augmentation schemes can ameliorate texture bias and improve generalization to out-of-distribution images. On the human side, existing studies have tested people under conditions different than those faced by a feedforward CNN; does the human—machine divergence remain when testing conditions are more fairly aligned? In experiments using brief stimulus presentations, we find that people do still privilege shape over texture. Even so, texture information plays more of a role than previously reported. This work establishes a new benchmark for assessing how "human-like" feedforward vision models are in their shape bias. Shape and texture are two features that are both useful in predicting an object's class. Zooming out, I will study models' treatment of such redundant features in a more general setting. Using synthetic data to explore which input features models learn as a function of their task relevance and difficulty of extraction, we find that CNNs are vulnerable to "feature blindness", privileging a single useful feature even when two features perfectly and redundantly predict image labels. Which of the features is privileged is predictable from the untrained model. Finally, I will discuss challenges and open questions that remain in the quest to build models with human-like visual representations

Keywords: Psychology.
Advisor: McClelland James L.
Grill-Spector Kalanit
Yamins Daniel