VictorJuiz/thesis huggingface dataset:
CRISPR-Cas enzymes as DNA sculptors

In the canonical mode of protein:DNA recognition, amino acids make specific contacts to nucleotides of a native DNA double helix. As RNA-guided gene editors and bacterial immune sentinels, CRISPR-Cas nucleases (clustered regularly interspaced short palindromic repeats, CRISPR-associated) break this mold, identifying target sequences instead through the formation of an ~20-base-pair R-loop. Requiring dramatic disruption of the DNA helix, this unique recognition mode is the basis for CRISPR’s powerful applications in research, medicine, and agriculture, but its physical underpinnings are poorly understood. In my thesis work, I studied the mechanisms by which Cas9 and Cas12 “sculpt” DNA to achieve their programmable solution to sequence-specific DNA binding and cutting. In Chapter 2, I describe my discovery that Cas9 bends and twists DNA to interrogate its sequence, a search mechanism that is likely shared by all DNA-targeting Cas enzymes. These DNA-bending actions, repeated over and over, constitute the slowest phase of genome editing, urging research into the rules that translate bending dynamics into target-capture rate. In Chapter 3, I present a yeast-display system designed to direct the evolution of fast-searching Cas mutants, both for basic inquiry into bend-assisted target search and for direct technological use. Finally, in contrast to Cas9, which forms double-strand breaks using two distinct nuclease active sites, Cas12 achieves the same feat using a single active site, necessitating further manipulation of DNA even after successful target capture. In Chapters 4-5, I show that Cas12 induces conformational plasticity in its substrate by exploiting a strand exchange junction with special structural properties, reshaping DNA in a way that maximizes the utility of minimal enzymatic machinery. While popular descriptions of CRISPR generally highlight its power as a pair of molecular scissors, my work demonstrates that the true functional novelty of these enzymes emerges from their skill as molecular sculptors.

Advisor: Doudna, Jennifer A
Kuriyan, John
A Matter of Good Taste: The Antecedents, Mechanisms, and Consequences of Social Class Signaling

Scholars across the social sciences have detailed how class-related norms, tastes, and social expectations—termed cultural capital—represent potent markers of class identity and create symbolic borders between classes. A crucial hypothesis stemming from this foundational work is that, in addition to reflecting social position, cultural markers help to produce it. In this dissertation, I present the results of five studies that provide insight into how those from upper-class backgrounds use culture to both help constitute their own identities and acquire a disproportionate share of prestigious accolades and opportunities. Over the course of the first three studies, I revealed that the preference for “highbrow” tastes for cultural products such as music and films is a conscious and strategically observable behavior among upper-class individuals, manifesting only in symbolic (public) aspects of one’s identity (Study 1), in situations where others are present (Study 2), and potentially in contexts where there is motivation to maintain a position of identity divergence from lower-class individuals (Study 3). The final two studies shed light on why upper-class individuals engage in such overt presentations. Specifically Study 4 demonstrates that individuals signaling highbrow cultural capital are perceived as wealthier, more competent, and more deserving of a prestigious occupational role compared to a target signaling popular cultural capital. Then, in Study 5, I leveraged an audit experiment in which emails were sent to admissions counselors at colleges and universities across the U.S., ostensibly from a high school student seeking application guidance, to reveal that counselors were more likely to respond to students signaling highbrow extracurricular activities, particularly counselors from more expensive institutions. Furthermore, counselors who did respond expended greater observer-rated effort in their responses when the student was signaling highbrow cultural capital. Overall, these findings reveal that signals of cultural capital can be potent sources of inequality maintenance, legitimization, and expansion.

Advisor: Keltner, Dacher
Computational Woodworking: Tools for Designing Stable Decorative Joints and Flexible Kerf Patterns

Traditional wood furniture often features decorative intrinsic joints and beautifully curved parts created with woodworking techniques. To craft such furniture, experienced woodworkers usually begin by drawing a visual design that describes not only the overall shape of the furniture but also the detailed appearances of the joints and curved parts. They then manually verify whether their furniture design satisfies all the constraints imposed by the intended design scenario (e.g., furniture assembled via joints must be stable when put together) and redesign if needed. Finally, they fabricate the furniture with conventional woodworking tools./nThis traditional furniture crafting process demands experience in woodworking and has historically been inaccessible to amateur users. Encouragingly, the continuing advancement and increasing availability of CAD programs and CNC machines have lowered the entry to the visual design and physical fabrication steps of the furniture crafting process. But, neither CAD programs nor CNC machines help users verify whether their visual design of the furniture fulfills all the necessary constraints, even though such verification often requires advanced woodworking knowledge not commonly held by everyday users. As a result, the verification step of the crafting process presents a serious challenge that prevents average users from expressing their creativity in furniture design./nThis thesis explores ways to assist users with verifying their furniture designs and guide users towards visual designs that satisfy all the requisite constraints. We aim to develop algorithms that automate the difficult verification step, allowing users to focus on producing the visual design of their furniture rather than analyzing the validity of their design. This thesis introduces interactive design tools that integrate algorithms informed by woodworking techniques to enable users to create (1) stable furniture models assembled via decorative intrinsic joints and (2) curved furniture parts formed through flexible kerf patterns.

Advisor: Agrawala, Maneesh
Hartmann, Bjoern
Scalable Reinforcement Learning Systems and their Applications

The past few years have seen the growth of deep reinforcement learning (RL) as a new and powerful optimization technique. Similar to deep supervised learning, deep RL has demonstrated the ability to solve problems previously thought to be unapproachable with machine learning (e.g., fine motor control of robotics, sports and video gaming), and substantial improvements over heuristic solutions for existing problems (e.g., in systems optimization, e-trading, advertising, robotic control). Like deep learning, deep reinforcement learning is necessarily computationally intensive. Because of this, researchers and practitioners in the field of deep RL frequently leverage parallel computation, which has led to a plethora of new algorithms and systems./nThis thesis looks at deep RL from the systems perspective in two ways: how to design systems that scale the computationally demanding algorithms used by researchers and practitioners, and conversely, how to apply reinforcement learning to expand the state of the art in systems. We study the distributed primitives needed to support the emerging range of large-scale RL workloads in a flexible and high-performance way, as well as programming models that can enable RL researchers and practitioners to easily compose distributed RL algorithms without in-depth systems knowledge. We synthesize the lessons learned in RLlib, a widely adopted open source library for scalable reinforcement learning. We investigate the applications of RL and ML for improving systems, specifically the examples of improving the speed of network packet classifiers and database cardinality estimators.

Advisor: Stoica, Ion
Beloved City, Depraved City: Communist Takeovers and Socialist Transformations in North Vietnam’s Cities (1950–1958)

This dissertation examines the communist takeover of cities in North Vietnam and some of the most important socio-political transformations that took place in these cities in the 1950s. It aims to do three things. First, it puts into perspective the intricate but understudied relationship between the communist revolution and the city. Second, it aims to critically complicate the way urban takeovers in Vietnam are portrayed in the existing literature and to demonstrate how an understanding of communist takeovers in the 1950s can enrich the larger historiography on modern state formation in general, and communist state formation in particular, in Vietnam. Finally, this dissertation aims to contribute to the growing literature on state-society relations in the early DRV. The dissertation starts with the first takeovers by DRV forces of cities along the Sino-Vietnamese frontier in 1949–1950 and ends in 1958, on the eve of nationalization and agricultural collectivization. Drawing on archival documents, newspapers, literary works, memoirs, and oral histories, this work reconstructs the perspectives and actions of both the communist “liberators” and members of the society “liberated” by Ho Chi Minh’s forces. The general picture that this dissertation paints is that: Vietnamese communists met various difficulties and forms of resistance after taking over urban areas - it took Ho Chi Minh’s government several years to turn North Vietnamese cities into the socialist symbols that they became by the 1960s - but in the process of attempting to reorganize the cities and its population, communist bureaucrats, cadres, and soldiers were also transformed by urban life and culture.

Advisor: Zinoman, Peter
Offline Learning for Scalable Decision Making

The remarkable success of modern machine learning has arguably been due to the ability of algorithms to combine powerful models, such as neural networks, with large-scale datasets. This data-driven paradigm has been applied to a variety of applications from computer vision and speech processing, to machine translation and question answering. However, the majority of these successes have been in prediction problems, such as supervised learning. In contrast, many real-world applications of machine learning involve decision making problems, where one must leverage learned models to select optimal actions that maximize some objective of interest. Unfortunately, learned models can often fail in these situations, due to issues such as distribution shift and model exploitation. This thesis proposes methods and algorithms which are designed to handle these shortcomings in modern machine learning methods in order to produce reliable decision making agents. We begin in the area of reinforcement learning, where we study robust algorithms for offline reinforcement learning and model-based reinforcement learning. We discuss considerations for benchmarking offline reinforcement learning and off-policy evaluation, and propose a variety of domains and datasets designed to stress test state-of-the-art algorithms in the area. Finally, we study the more general problem of model-based optimization, and show how information-theoretic principles can guide us to construct uncertainty-aware models that mitigate exploitation.

Advisor: Levine, Sergey
Impacts of Chronic Circadian Disruption on Neural Structure and Cognition: The Importance of Considering Sex and Age

Circadian (from the Latin, circa-about; diem-day) rhythms temporally coordinate neural and hormonal systems on a daily schedule to maintain optimal health and functioning. In adults, disruptions to circadian functioning through poor sleep hygiene, night or rotating shift work, transmeridian travel and exposure to artificial light at night are associated with a number of disease states, including obesity and metabolic disease, diabetes, heart disease, cancer, deficits in cognitive functioning and reproductive dysfunction, among other maladies. However, whether circadian disruption negatively impacts typical development, leading to permanent deficits in brain physiology and behavior, has been minimally explored outside of early life disruption. In addition, most studies to date focus on male animal models whereas the present studies explore whether or not chronic circadian disruption (CCD) differentially impacts both sexes. The present studies identify adolescence as a period in which CCD has differential impact on brain and behavior depending on the sex of the organism. These findings further underscore the need for inclusion of both sexes in studies of CCD and to consider of the age at which disruption occurs./nIn this thesis I also apply the concepts of just science and good science to teaching undergraduate biology students about biological sex. Broadly, just science establishes that science is created in an objective vacuum outside of most social influences, and that implications of science arise from the interactions with society after the fact. An important caveat of why the concept of good science is a promising prospect to explore in students from the biological sciences is that it is carefully crafted to not be anti-science. Some students reported how they had initially thought biological sex was unchangeable or always addressed in biomedical research but, given the new information from scientists and feminists scholars, they had changed their view. Others remained adamant that biological sex cannot be changed regardless of a person’s identity. Thus, my findings suggest further exploration on bringing good science around biological sex to earlier stages of students’ college education, as opposed to be hidden in specialized and advanced courses. In summary this thesis explores the impact of addressing diversity and complexity both in biology research and instruction to not over simplify or look over the destructive biases that have been identified.

Advisor: Kriegsfeld, Lance J
Through the Fire and All the Smoke: Abolition, Anti-Capitalism, and Intersectionality in the Era of Black Lives Matter

This dissertation is a historical analysis of Black Lives Matter (BLM) and the movement for Black lives (MBL) in Los Angeles from 2013 to 2021. It is also a theoretical consideration of the prospects and complications of racial justice activism in the 21st century. It interrogates the political philosophies and organizational strategies of BLM/MBL. This study explores how the intervention of intersectional, intergenerational, and decentralized leadership forms have disrupted heteropatriarchal traditions in the Black freedom struggle. It evaluates the significance and utility of Marxism in addressing issues of economic crisis and state-sanctioned violence from the 2008 Great Recession to the COVID-19 pandemic. It also evaluates how BLM/MBL has interfaced with labor organizing and other racial justice developments like the #StopAsianHate, #AbolishICE, and Indigenous Sovereignty movements. Lastly, it examines the vital role of youth and student activism in college campuses and community-based non-profit organizations in both initiating BLM/MBL and in sustaining its mobilization./nKEY WORDS: Black Lives Matter, racial justice activism, abolition, anti-capitalism, intersectionality, Marxism, state-sanctioned violence, labor organizing, youth activism

Advisor: Taylor, Ula Y.
Feeling’s Forms: Theorizing Sentiment in Women’s Anti-Racism Poetry of the Long Nineteenth Century

My dissertation, "Feeling's Forms: Theorizing Sentiment in Women's Anti-Racism Poetry of the Long Nineteenth Century," centers the work of a group of overlooked and interconnected nineteenth-century women poet-activists. Diverse racially and culturally as well as in their artistry and activism, the poets I examine are brought together by two shared and interlinked goals: first, to elucidate the wrongs done to and advocate for the rights of enslaved Africans and disenfranchised Native Americans, and second, to continually and self-consciously renegotiate their use of and stance toward the sociopolitical efficacy of their poems' formal strategies. In particular, they are concerned with sentiment—a literary mode first beloved for its emotional appeal, then decried and dismissed as lachrymose, and now viewed with more nuance as both politically powerful and problematic in its single-minded focus on individual feeling. My project builds on this most recent appraisal, suggesting that contrary to the popular conception of nineteenth-century poets as writing from a wellspring of unexamined feeling, the poets I consider themselves offered theorizations that both presage and problematize contemporary critical understandings of sentiment. In dialogues both between and within their poems, these now-largely-forgotten poet-activists transform poetry and its attendant forms into a realm in which sentiment itself could be actively tested and questioned, beginning a conversation about art and activism that remains profoundly pertinent today.

Advisor: Schweik, Susan
Real World Robot Learning: Learned Rewards, Offline Datasets and Skill Re-Use

Robots that can operate in an open, unstructured environment and perform a wide range of tasks have been a long-standing goal of artificial intelligence. For such robots to operate effectively, they need the ability to a) perceive the world around them through general-purpose on-board sensors like cameras b) generalize to new situations c) improve their performance as they collect more data. In this thesis, we posit that deep reinforcement learning (deep RL) methods are well-positioned to overcome the aforementioned challenges, but are difficult to apply to real world domains like robotics. The central conjecture that we study in this work is the following: while dominant robot learning pipelines often rely on hand-engineering certain components (such as reward functions and physics simulators), we can overcome many bottlenecks of these pipelines via the adoption of a more data-driven perspective. We argue that, instead of hand-engineering reward functions, we should instead learn reward functions from data. Instead of learning mostly in a hand-designed simulation and then transferring learned policies to the real world, we should learn using real data, and re-use all past experience (as much as possible) to maintain sample efficiency. We show how this perspective change greatly simplifies robot learning, and demonstrate results on a variety of real world object manipulation tasks.

Advisor: Levine, Sergey
Optimization Models and Methods for Large Scale and Complex Systems, with applications to distributed electricity resources integration

This thesis focuses on models and methods for large scale and complex systems, with applications to distributed electricity resources. It is organized into three parts. In the first part, we show how to formulate the participation of distributed electricity resources -- consisting of plug-in electric vehicles and residential photovoltaic panels -- in the real-time and day-ahead electricity markets using convex optimization models. We show how these large scale problems can be efficiently solved by distributing them using a dual splitting approach. Using this structure, we then derive algorithms that allow to solve these problems efficiently and study their convergence properties. Finally, we illustrate our approach with multiple study cases on different markets. In the second part, we develop novel first-order heuristics methods, called Hopfield methods, based on Hopfield Neural Networks; in order to find candidate solutions to large-scale combinatorial optimization problems. We study the geometry and the convergence of these new methods and show how they connect with known convex optimization models and algorithms. We then illustrate how these methods perform on large nonlinear problems. In the last part, we present a new class of optimization models, implicit optimization, which includes deep learning, nonlinear control, and mixed-integer programming as special cases. Implicit optimization provides a unified perspective on these different fields, leading to new algorithms and surprising connections. We propose two tractable algorithms to solve such problems based on their implicit equation structure: implicit gradient descent and the Fenchel alternative direction method of multipliers. We illustrate our theory and methods with numerical experiments and dedicate a whole chapter on implicit deep learning architectures and methods.

Advisor: Moura, Scott J
El Ghaoui, Laurent
On the temporal and spatial flow scales in the near field of homogeneous and immiscible round turbulent jets through image-based measurements

The work being presented here concentrates on characterizing flow scales of turbulent jets in the near field. Experiments were carried out with water jets and immiscible silicone oil jets of two viscosities submerged in a water tank. The jet Reynolds numbers are in the range of Re ~ 4500 - 50000 for homogeneous water jets and Re ~ 3500 - 27000 for silicone oil jets in water. To observe the turbulent/non-turbulent interface, the jet fluids are made visible by doping with fluorescent dye and excitation with directional illumination. The jet interfaces are continuous and convoluted for water jets, whereas the interfaces of the oil jets are convoluted and discontinuous with droplets and ligaments. Direct flow visualization, schlieren photography, shadowgraph photography and particle image velocimetry are employed as appropriate. Interfacial length scales are characterized using various image processing techniques for both water and oil jet runs. For the homogeneous water jet runs, streamwise internal length scales of the schlieren recording are investigated after applying temporal filters to isolate scales within the flow. When isolated based on their temporal signatures, observations of the homogeneous water jets show that the length scales of internal features increase with Reynolds number. As the Reynolds number increases, the temporal signatures of the features within the jet can be seen populating higher frequency modes. Droplet sizes for the immiscible jet runs are quantified using Hough transformation. Interface length scales decrease with Reynolds number and increase gradually with distance from the exit plane for a given Reynolds number. These scales are isotropic for the homogeneous water jets and exhibit a streamwise to cross-stream ratio of about 1.3 for the oil jets. Results indicate the average droplet size in the immiscible jets is determined by the interfacial surface tension, in relation to the Weber number.

Advisor: Savaş, Ömer