VictorJuiz/thesis huggingface dataset:
Data science : a gateway to belonging in STEM and other quantitative fields

The content of secondary mathematics leaves much to be desired in terms of student interest, but also in terms of its representativeness of real world mathematics, leaving students with only a limited glimpse of how math is applied in the world and perhaps prematurely deciding that the majors and careers that mathematics leads into are not of interest to them. Contrarily, data science is inherently real world, because the data itself comes from real contexts with authentic problems to solve. For example, students can explore how machine learning functions in music recommendation systems, or how data can be used to investigate skin tone representation in social media advertisements. In addition, data science can be learned and explored in contexts that are personally interesting and valuable to students. For these reasons, data science has the potential to capture the interest and engagement of mathematics students who are motivated to learn content that they find interesting and useful in ways that other mathematics coursework cannot. This study looks at the ways in which students' experience in a high school data science course served to increase their interest in pursuing majors and careers in STEM

Keywords: Education.
Advisor: Boaler Jo
Langer-Osuna Jennifer
Williamson Peter
Data-driven methods for characterizing temporal variation in agricultural water use

Satellite remote sensing provides a consistent way to monitor water use over large agricultural regions with field-scale resolution. Maximizing the value of this data, for both water managers and water users, requires methods for identifying temporal variation in irrigation practices and evapotranspiration (ET) more generally. Chapter 1 provides motivation and context for the problem of ET estimation for monitoring agricultural water use. Chapter 2 analyzes sources of ET estimation error introduced by meteorological and land surface data inputs, evaluated at three winegrape vineyards in California. Chapter 3 presents a method for identifying the point-in-time irrigation status of a field without labeled training data or ancillary weather or geophysical data inputs. Chapter 4 presents a method for characterizing stages of crop development using multivariate satellite inputs, applies it to the problem of crop planting date estimation, and also uses the method to illustrate structural properties of intra-annual environmental time series. Together these projects advance the theory and practice of quantifying agricultural water use with satellite remote sensing

Keywords: Engineering.
Advisor: Mauter Meagan
Kitanidis P. K. (Peter K.)
Konings Alexandra
Data-driven sequential decision making by understanding and adopting rational behavior

A remarkable feature of an intelligent agent is the ability to make sequences of smart decisions that are executed in coordination to reach goals. As can be seen by watching humans, a polished sequential decision making policy yields elegant behaviors such as smooth driving, dexterous locomotion, and prudent investments. Learning optimal policies for sequential decision making is challenging due to issues such as the difficulty of long-horizon credit assignment, exploration in exponentially large search spaces, and designing suitable reward functions to encourage the correct behavior. In this dissertation, we are interested in, perhaps, one of the most natural forms of learning that humans engage in: learning from observations. We would like to focus on algorithms that enable data-driven learning of sequential decision making policies by observing optimal behavior demonstrated by other rational agents. This process comprises two main steps: understanding and adoption. In the first part, we discuss how to design algorithms that allow an agent to understand and thus internalize rational behavior. We develop an active world model learning algorithm that enables an ego-agent to build models of complex behaviors demonstrated by human-like animate agents by efficiently directing its attention. We further investigate the feasibility of building models of other rational agents by Inverse Reinforcement Learning. In the second part, we develop methods to adopt rational behavior from demonstrations. We develop algorithms for Imitation Learning in the presence of domain mismatch such as morphological and viewpoint differences. We further propose algorithms for imitation via Inverse Reinforcement Learning where we propose algorithms that extract underlying rewards from demonstrations of complex behaviors such as robotic locomotion. We hope that these contributions bring us one step closer to solving real-world sequential decision making problems with machine learning.

Keywords: Engineering.
Advisor: Ermon Stefano
Haber Nick
Sadigh Dorsa
Data-driven statistical sharding for industry-scale neural recommendation

Deep learning based recommendation models (DLRMs) form the backbone of many internet-scale services such as web search, social media, and video streaming. Primarily composed of massive embedding tables, potentially terabytes in size, these models require immense system resources to train and the solving of the sharding problem. The sharding problem is the task of partitioning and placing the embedding table parameters throughout the target system memory topology such that training throughput is maximized. This dissertation: (1) Characterizes and derives statistics from DLRM training data which can be used to accurately and granularly predict the memory demands of individual embedding table rows; (2) Presents RecShard, a mixed-integer linear program based approach which uses these statistics to solve the sharding problem for capacity constrained single-node systems, where parameters must be placed across high-performance GPU HBM and much slower CPU DRAM; reducing accesses to the latter by orders of magnitude; and (3) Presents FlexShard, a precise row-level sharding algorithm which focuses on sharding emerging sequence-based DLRMs across multi-node GPU training clusters; leveraging these statistics to significantly reduce inter-node communication demand, the bottleneck of scale-out DLRM training

Keywords: Engineering.
Advisor: Kozyrakis Christoforos
Trippel Caroline
Wu Carole-Jean
Data-driven sustainability : advancing electric vehicle adoption and carbon accounting using artificial intelligence and geospatial analytics

This dissertation explores data-driven, computational techniques for sustainability decision-making, particularly focusing on the transportation sector and the carbon accounting landscape. Given the urgency of large-scale decarbonization efforts underscored by the Intergovernmental Panel on Climate Change, this work proposes and evaluates methods to help decision-makers navigate complex interactions between humans, engineering systems, and ecological systems, taking into account factors such as data accessibility and uncertainty. The dissertation is divided into two parts. The first, encompassing chapters two and three, delves into how computational techniques and data can expedite the decarbonization of the transportation sector. The second part, which includes chapters four and five, emphasizes elevating societal understanding and accounting of carbon emissions in the context of sustainable decision-making. Altogether, this dissertation underscores the importance of comprehensive, computationally-enhanced, data-driven, and uncertainty-aware approaches to sustainability decision-making, thus providing practical pathways for effective climate change mitigation.

Keywords: Engineering.
Management Science and Engineering.
Advisor: Weyant John
Heller Thomas
Rajagopal Ram
De facto school discipline and the maintenance of inequality

Like many American institutions, K-12 schools are attempting to shift away from punitive measures and embracing a rhetoric of restorative justice. In this dissertation, I draw on three years of ethnographic fieldwork and interviews in a racially and economically diverse public school, Northwest High, to answer the following question: how does the institutional shift towards non-punitiveness unfold in practice? I highlight how this shift corresponds with a shift in governance strategy. Amidst the non-punitive turn, institutions increasingly seek to govern by connecting their constituents with resources rather than enacting harsh punishment. At Northwest High, staff address challenges by connecting students and their families to goods and services in attempt to reduce their use of exclusionary discipline like suspensions and expulsions. As such, the school becomes a crucial site for the distribution of social services. This resource brokering can have many benefits. It can also maintain inequality. I show how resource brokering is intertwined with surveillance and discipline. Importantly, school staff lack the capacity to successfully serve as brokers for all their constituents. Staff therefore navigate the constraint of limited resources by strategically managing the boundaries of the institution, redefining which children get to remain a member and thus who they will continue brokering for. I then explain why these forced removals -- which I call de facto expulsions -- typically occur through informal rather than formal means. I argue that informal methods of exclusion become favored in the non-punitive turn because organizational leaders seek to submit low official counts of discipline to the state accountability system. These processes sustain discipline inequality. Students who depend more on the school to access goods and services - many of whom are poor or recent immigrants - are more likely to become perceived as a burden and be expelled. Because these removals often occur informally, punishment becomes invisibilized and official data undercount the number of students forcibly removed from their schools. Finally, I document that even absent institutional consequences, informal discipline can harm young people and their families by challenging their self-concept and altering their social ties. I conclude by considering how we might maximize the positive benefits of school resource brokering while minimizing the negative, often unintended consequences

Keywords: Humanities and Sciences.
Sociology.
Advisor: Jackson Michelle Victoria
Stuart Forrest
Torche Florencia
Reardon Sean F.
Dealing with a deadly diet : molecular mechanisms of alkaloid sequestration in poison frogs

Chemical defense is a widespread phenomenon where organisms employ toxic compounds as a means of protection against predators. Poison frogs (family Dendrobatidae) sequester an array of alkaloid toxins onto their skin from their diet for chemical defense, and often possess aposematic signals warning potential predators of their toxicity. Although the skin alkaloids, coloration, and dietary resources of poison frogs have been rich areas of research in the last 40 years, little is known about the molecular mechanisms involved in their ability to uptake alkaloids from their diet. My dissertation focused on applying comparative biochemical and genetic approaches to better understand the molecular processes involved in toxin sequestration, metabolism, and autoresistance. In Chapter 2 I investigate the effects of dietary pumiliotoxin 251D (PTX) on gene expression and identify a candidate cytochrome P450 protein involved in PTX hydroxylation. Chapter 3 focuses on an unlikely member of the serine protease inhibitor family, alkaloid binding globulin (ABG), as a transporter of alkaloids in poison frog plasma. In Chapter 4 I compare the alkaloid and transcriptomic profiles of toxic, moderately toxic, and non-toxic poison frogs, unraveling the molecular correlates of toxicity and autoresistance. This work expands our understanding of PTX metabolism, identifies potential candidate enzymes involved in alkaloid hydroxylation, and sheds light on the multifaceted physiological roles played by serpin proteins, exemplified by ABG. Furthermore, we challenge the conventional notion of toxicity as a binary trait and suggest that gene expression plasticity may be a key aspect of poison frog evolution. Potential avenues for future work include testing poison frog cytochrome P450 proteins for metabolic activity, exploring the functional diversity of serpin proteins, and more comprehensive analyses of poison frog genomes, transcriptomes, and alkaloid profiles. Overall, we applied experimental techniques to better understand the molecular underpinnings of chemical defense in poison frogs, paving the way for future work in poison frog physiology.

Keywords: Humanities and Sciences.
Biology.
Advisor: O'Connell Lauren
Cyert Martha
Du Bois Justin
Long Jonathan
Death is a feast : the ideology of the homeric banquet

In large part, feasting in the Iliad and the Odyssey has been critically overlooked by studies on the political ideology of the Homeric poems. This dissertation studies the meaning of the feast, δαίς, in these poems and contends that the feast in Homeric poetry is what the anthropologist Bryan Hayden has called "a critical element, almost entirely overlooked in the past." Feasting, as the anthropologists Bryan Hayden and Michael Dietler have shown, forms critical reciprocal obligations which encompass "the political, economic, ritual, and social universe." Likewise, I argue that feasting reveals the reciprocal obligations at the heart of the political, economic, and ritual world of Homeric society. By providing an updated formulaic and linguistic analysis of the language of feasting in Homer, the "critical" social relations of the Homeric community are revealed. Part I one of this dissertation provides an exhaustive formulaic analysis of two contrastive verbs used to describe feasting: (1) δατέομαι, "to divide up, share, " and (2) δαίνυμι, "to feast." Part II analyzes the formulaic meaning of the δαίς, "feast, " as a particular conceptual form of division. I show that the δαίς, derived from the Indo-European root, *deH2-, "to divide, " is not a "meal" that embodies egalitarian participation, but is instead a gift that instantiates an ideal form of hierarchy (social division). This study resolves contradictory definitions in the Homeric lexicography of feasting by showing how they rely on incomplete and partial conceptualization of the Homeric idea of division. Finally, this study concludes by re-contextualizing the Homeric feast within the anthropological study of feasting. It contends that the reciprocal obligations of the feast reflect a singular vision of ideal power and community: single patron rule. This allows us a unique perspective on the question of the political ideology of Homeric poetry. Instead of the struggle between aristocratic and plebiscitary democratic ideology, it is suggested that the Homeric feast reflects an ideological struggle between royal tributary rule (Odysseus' patron feast) and the rise of oligarchy (the Suitors' symposium) in the 8th to 6th century BCE.

Keywords: Humanities and Sciences.
Classics.
Advisor: Martin Richard P.
Morris Ian
Nightingale Andrea Wilson
Deciphering regulatory DNA with deep learning models and interpretation methods

Accurate predictive modeling of gene regulation is crucial for a fundamental understanding of cell identity and function. High-throughput profiling of diverse biochemical and functional properties of cells has enabled powerful deep learning based DNA sequence models that predict protein-DNA binding, chromatin accessibility and histone marks across cell types with state-of-the-art accuracy. Interpretation of these DNA sequence models has revealed novel insights into the cis-regulatory code of TF binding, effects of sequence variation and repeats, and the sequence basis of chromatin accessibility. However, there is much scope for enhancing modeling strategies, model performance, and the tooling and infrastructure around model development and interpretation. Moreover, the full potential of these models for extracting biological insights from high-throughput functional profiling data has not been realized. In this thesis, I will present novel methods that advance DNA sequence models for regulatory genomics, and some applications of these DNA sequence models to glean insights into biological systems. First, I introduce ChromDragoNN, a method that enables generalization of DNA sequence models to make predictions in new cell types. Next, I describe fastISM, an algorithm to significantly speed up variant scoring for convolutional neural networks. I then present dynseq, a tool for sharing and visualization of model-derived importance scores of individual bases. I will then apply DNA sequence models to two different biological systems. First, I combine single-cell chromatin accessibility profiling with DNA sequence models to nominate regulatory DNA variants associated with eye disorders. Next, I apply DNA sequence models to the study of single-cell chromatin accessibility from a time course of human skin cells transforming into induced pluripotent stem cells over four weeks. Using DNA sequence models, I reveal mechanistic insights into reprogramming progression by linking transcription factor abundance changes to sequence logic encoded in regulatory elements. Together, this thesis advances predictive modeling and analysis of gene regulation through new methods, tools and biological applications. I hope that the work moves the field closer to realizing the full potential of DNA sequence models for understanding cell identity and function.

Keywords: Engineering.
Advisor: Kundaje Anshul
Engreitz Jesse
Horowitz Mark (Mark Alan)
Deep learning based LArTPC event reconstruction and nonmagnetic muon sign determination in the ICARUS detector

The Liquid Argon Time Projection Chamber (LArTPC) is a state-of-the-art particle imaging detector technology pioneered at the kiloton scale by the ICARUS detector, now at Fermilab. It will be widely used in accelerator-based oscillation experiments for precision measurements of neutrino oscillation parameters. This thesis demonstrates a novel end-to-end, deep-learning-based data reconstruction chain for LArTPC. This approach can scale up to large existing and upcoming LArTPC neutrino detectors such as ICARUS and DUNE with sparse convolution techniques. We apply it to an electron neutrino selection on ICARUS simulation, which shows promise for a future low-energy excess search. This work further presents two methods for nonmagnetic muon sign determination in ICARUS: comparing the muon decay electron energy spectrum with bound muon decay-in-orbit numerical predictions, and fitting the muon decay time distribution. ICARUS cosmic ray data analysis yields a muon to antimuon ratio of 0.69 +/- 0.12 in agreement with previous measurements. Such methods for statistical separation between muon and antimuon, when applied to a muon neutrino/antineutrino beam, are expected to improve the sensitivity of ICARUS and forthcoming LArTPC experiments to CP-violation, neutrino mass ordering, and neutrino oscillation measurements

Keywords: Humanities and Sciences.
Physics.
Advisor: Tanaka Hirohisa
Terao Kazuhiro
Tompkins Lauren Alexandra
Deep Learning for Seismic Resolution Enhancement, Enhancing Low-Resolution Legacy Data Using High Resolution Guidance

This study investigates the utilization of the CycleGAN neural network architecture to potentially enhance resolution in subsurface seismic imaging. While convolutional neural networks (CNNs) have been extensively applied for image enhancement, prior research mostly focused on increasing pixel resolution for detailed image enhancement. However, this thesis uniquely targets vertical and horizontal resolution issues in seismic data, often leading to undetected geological features largely due to insufficient frequency content in recorded seismic data. Enhancing subsurface seismic images involves significant resource costs, but this thesis showcases the potential to mitigate these costs by employing machine learning to enhance older-generation seismic data. Utilizing 3D seismic data from Saudi Arabia, including both older and higher resolution datasets, the research aims to address the frequency content and processing algorithms differences in historical datasets by training a CycleGAN model with a U-net as the CNN. The model aims to convert legacy seismic images to closely resemble higher-resolution datasets. Initial findings reveal noticeable resolution improvements, yet further refinement opportunities exist.

Keywords: Saudi Aramco
Seismic
Deep learning (Machine learning)
Image processing
Text
Thesis
Advisor: Mukerji Tapan
Deep learning on a diet : an error landscape perspective on parameter and data efficiency in deep learning

Many of the recent remarkable breakthroughs in artificial intelligence have come from scaling up both the number of parameters in artificial neural networks (ANNs) and the size of datasets used to train them. This scaling however is unsustainable and it is important to develop methods to achieve similar results under resource constraints. But when and how can we decrease the number of parameters and examples while still training ANNs to the same performance? We investigate methods for pruning both network parameters and the dataset from the perspective of the optimization error surface for ANNs. Through an empirically driven investigation, we show how geometric properties of the error landscape, such as curvature and error basins, determine how many parameters can be pruned and which examples are important for generalization. Overall, we take a step towards understanding the scientific principles that underlie data and parameter efficiency in ANNs.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Ganguli Surya
Druckmann Shaul
Yamins Dan
Demanding Beauty: A Critical Analysis of Quality of Life for Women with Facial Disfigurement

Facial disfigurement has been found to be associated with poverty, social exclusion, low self-esteem, and an overall diminished quality of life compared to those without facial disfigurement (a.k.a. facial difference). Despite the lack of clear evidence on whether surgical interventions can enhance the quality of life of individuals with facial disfigurement, many medical practitioners still offer such treatments in an effort to improve their patients' well-being. This research sought to examine under what conditions treatment for facial disfigurement does indeed lead to a better quality of life. The study revealed that women who felt they had a sense of agency in choosing to receive treatment were more likely to report a high quality of life. Additionally, these women reported feeling positive about their overall healthcare experience and feeling highly respected by others. This paper concludes by discussing the implications of these findings for gaining useful insight into how resources can be improved to help meet the disfigurement community’s needs.

Keywords: Quality of Life
Facial disfigurement
Facial difference
Plastic &amp; Reconstructive Surgery
Craniofacial Abnormalities
Deformities
Text
Thesis
Advisor: Luhrmann Tanya