VictorJuiz/thesis huggingface dataset:
Advancing resource recovery following anaerobic secondary treatment of domestic wastewater

Proper treatment of domestic wastewater is crucial for protecting human health and the environment. However, conventional wastewater treatment processes are often high-cost, energy-intensive, and insufficient for recovering resources. Furthermore, water infrastructure in the United States is nearing the end of its intended design lifespan, posing a key opportunity for reinvention. Anaerobic secondary treatment is a promising example of next-generation water infrastructure that prioritizes resource recovery through the production of methane energy. However, anaerobic secondary effluent requires further attention due to the presence of dissolved methane, sulfide, nitrogen, and phosphorus. This dissertation explores post-treatment of anaerobic secondary effluent to maximize resource recovery from domestic wastewater. Specifically, a life cycle assessment was performed to evaluate tradeoffs between physical/chemical processes and biological processes for dissolved methane, sulfide, nitrogen, and phosphorus removal. Additionally, a membrane-aerated biofilm reactor was tested to treat anaerobic secondary effluent with high concentrations of ammonium-nitrogen and sulfide. Lastly, the use of wastewater-derived struvite as a novel fire retardant was explored to improve the profitability of phosphorus-recovery technologies. These studies serve to direct future efforts in developing complete water treatment trains with anaerobic secondary treatment

Keywords: Engineering.
Advisor: Criddle Craig
Luthy Richard G.
Tarpeh William
Advancing RNA therapeutics : regulation, immunogenicity, and delivery

The Central Dogma of molecular biology formalized the significant role of RNA in regulating the flow of information from DNA to proteins. However, beyond its canonical function in translation, RNA carries a wide array of functions, serving as a molecular machinery that regulates gene expression during development and cellular differentiation. Furthermore, the remarkable success of RNA-based therapies in combating the COVID-19 pandemic has sparked greater interest in the translational potential of this remarkable molecule. The renewed enthusiasm in RNA medicine comes from its simple design, safe and relatively fast manufacturing, and, most importantly, its diverse molecular formats, capable of reaching formerly deemed 'undruggable' targets at the DNA, RNA, or protein levels. Furthermore, the large variety of RNA molecules, including the most recently described class, known as circular RNA, uncovers many new biological functions and therapeutic applications. Nevertheless, the development of RNA therapeutics still remains hampered by significant challenges. These encompass the rapid degradation of exogenous RNA by ubiquitous RNases, the difficulty of transporting negatively charged RNA through hydrophobic cytoplasmic membranes, and the potent immunogenicity of exogenous RNA, leading to cellular toxicity and hindering its translation into therapeutic proteins. My dissertation addresses these hurdles, exploring mechanisms of RNA delivery for vaccine development and cell reprogramming. Chapters delve into circular RNA uptake, immunogenicity, and biodistribution, proposing novel nanoparticle transporters for targeted mRNA and circular RNA delivery. The research aims to transform vaccine development and cell therapy by enhancing circRNA delivery.

Keywords: Medicine.
Stem Cell Biology and Regenerative Medicine.
Advisor: Chang Howard Y. (Howard Yuan-Hao)
Majeti Ravindra
Pulendran B. (Bali)
Advancing the performance of multi-physics heart simulations

Cardiovascular diseases (CVDs) are the leading cause of death globally, accounting for an estimated 17.9 million deaths in 2019. CVDs are a group of disorders that affect the heart and blood vessels, and include coronary heart disease, stroke, heart failure, and arrhythmias. Computational models of the heart offer a powerful tool to understand the mechanisms of CVDs and develop new diagnostic and therapeu- tic strategies. Finite element method (FEM) is a widely used numerical method for solving complex engineering problems, and has been successfully applied to model the heart. In this thesis, we used FEM to develop computational models of the heart to understand disease mechanisms, study model parameters to understand their ef- fects on the simulation results, and develop a novel constitutive material model to improve accuracy of the simulations. We first studied the effects of viscoelastic model parameters on the simulation results of healthy heart model, in order to understand the trade-off between increased model complexity and increased accuracy of the sim- ulations. We also developed a patient-specific computational model of a diseased pediatric heart. We used this model to study the mechanisms playing role in the progress of this disease and to evaluate possible treatment options. Finally, we devel- oped a novel constitutive material model for the heart muscle. This model is more accurate than existing models, and can better capture the complex behavior of the heart muscle. Our work demonstrates the potential of computational modeling to improve our understanding of CVDs and develop new diagnostic and therapeutic strategies. Given the scale of the CVD epidemic, it is essential to continue to invest in research on computational modeling of the heart.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Kuhl Ellen
Ennis Daniel B.
Marsden Alison (Alison Leslie)
Adversity roadmap : improving psychological well-being and resilience through a values-affirmation approach

In this dissertation, I created a psychological roadmap for navigating adversity by grounding people in their core values. Research trends and global events have underscored the enduring significance of well-being and mental health. Central to this study is the self-affirmation theory, which proposes that humans possess an intrinsic need to perceive themselves as morally and adaptively capable, even when confronted with challenges. My objective was to tailor values-affirmation-based interventions to specific contexts and target groups, with the aim of enhancing well-being and motivation-related outcomes in the general population (Study 1), elite athletes (Study 2), and college students (Study 3). Study 1 involved a three-stage longitudinal randomized experiment conducted during the initial months of the COVID-19 pandemic. The study evaluated the effectiveness of two versions of the values-affirmation intervention in mitigating pandemic-related psychological burdens, particularly the feelings of social disconnection and loneliness. Notably, the interventions proved most beneficial for men, who exhibited the sharpest decline in social belonging and mental health. In Study 2, I delved into the world of high-performance athletics. I identified two root causes of compromised psychological well-being among professional Dancesport athletes -- (1) high psychological stress, and (2) outcome-contingent self-worth. To address these challenges, I developed a novel "Resilience Training + Affirmation" (RTA) intervention. Results demonstrated promising advantages of RTA for athletes' mental health and self-efficacy. Furthermore, once the competitive stress had subsided, RTA bolstered athletes' sense of social belonging and reinforced their dance identities. The findings also indicated the RTA's potential to enhance resilience among Dancesport athletes, even in the face of unsatisfactory competition results, thereby mitigating the risks associated with outcome-dependent self-worth. Study 3 extended the insights from Study 1 to an educational context. By integrating established values-affirmation techniques with actionable plans, I aimed to assist students in establishing tangible links between their values and everyday experiences. I explore unexpected correlations between well-being outcomes and pertinent covariates, such as minority race, gender, social class, and first-generation status. My aspiration is that the discoveries outlined in this dissertation, along with future research endeavors, will pave the way for the development of finely tuned strategies capable of enhancing various facets of the human experience across diverse domains.

Keywords: Education.
Advisor: Cohen Geoffrey
Dweck Carol
Fisher Philip
Zaki Jamil
Against productivity : unproductive writing as resistance in early Latin American fiction

This dissertation examines Latin American novels from the 1840s through the 1920s in which characters use writing as a means to resist oppression. While acknowledging that in the period of nation-state consolidation, writing worked as a colonial imposition (Mignolo), a tool for neocolonial domination (Rama), and a means of social indoctrination (Sommer), I argue that certain writing practices of the time were rebellious and "unproductive" in that they worked against the economically productive interests of slavery-based and emerging capitalist societies. I show that Gertrudis Gómez de Avellaneda's Sab (Cuba, 1841), José de Alencar's The Guarani (Brazil, 1857), Joaquim Maria Machado de Assis's The Posthumous Memoirs of Brás Cubas (Brazil, 1881), and Teresa de la Parra's Iphigenia (Venezuela, 1924) depict disenfranchised subjects (clerks, homemakers, enslaved peoples, former Indigenous leaders) writing in minor genres (notes, letters, scribbles, diaries) to take a stance against racial, gendered, and social norms, as well as to escape the oppressive experiences of domestic, pauperized, and forced labor. Given the paucity of archival evidence documenting penning habits in the national consolidation era, novels serve as privileged sources to interrogate how nineteenth-century and early-twentieth-century Latin Americans conceived of the writing process and experienced its liberating power. By considering the freeing function that writing adopts in early national novels, my research shifts the conversation away from the binaries that have dominated the fields of literary, cultural, and historiographical studies of Latin American societies, including writing/orality, original/derivative, local/colonial, and foundational/afoundational. I move away from binaries by demonstrating that in nineteenth and early-twentieth-century Latin America, acts of writing were not mere colonial impositions of the Spanish and Portuguese Empires, but also practices that might have historically worked alongside oral traditions to turn colonial legacies and their successor neocolonialist orders against themselves. Following early novelists, writing may have served to oppose experiences of enslavement, migration, solipsism, and physical confinement and to creatively resist the expectation of "productivity" by socioeconomic systems

Keywords: Humanities and Sciences.
Iberian and Latin American Cultures.
Advisor: Hoyos Ayala Héctor
Briceño Ximena
Peluffo Ana
Saldívar José David
Algorithm-driven paradigms for the optimization and simulation of photonic devices

The world has witnessed transformative advancements in photonics, encompassing a broad range of applications from AR/VR glasses to LiDAR technology employed in self-driving cars. These captivating applications heavily rely on the simulation (forward problem) and optimization (inverse problem) of photonic devices. However, both the forward and inverse problems present their own set of challenges: 1) For forward problems, how to rapidly and accurately compute electromagnetic field distributions within structured media, especially when evaluating large simulation domains and large batches of devices. 2) For inverse problems, how to efficiently identify the overall optimal device in a non-convex design space while incorporating physical and fabrication constraints. This thesis discusses how algorithms based on classical optimization and deep learning are able to overcome the aforementioned challenges and are establishing a new conceptual framework for freeform photonic engineering. In the first half of the thesis, we introduce how a design space reparameterization scheme is able to simplify the inverse design problem and can naturally enforce hard fabrication constraints in adjoint-based freeform topology optimizations. The combination of device capability, feature size constraints, and ease of manufacturability enabled by this methodology facilitate our design of robust, high performance metasurfaces such as metagratings, metalenses, and metapolarizers. In the second half of the thesis, we present a high accuracy and ultra-fast surrogate electromagnetic simulator for the forward problem that is based on physics-augmented deep learning. A central feature of this neural network surrogate simulator is the utilization of data and physics to model and exploit high-dimensional relationships between geometric structure and electromagnetic response within the constraints of Maxwell's equations. Finally, by combining the high-speed forward simulators with the reparameterization inverse design scheme, we showcase hybrid ultrafast freeform optimizers that are able to effectively search for local and global optima of dielectric nanostructures with a speed-up of three to four orders of magnitude. We anticipate that these algorithmic approaches to photonic engineering will empower practitioners to move away from asking \emph{how} to realize a photonic system and focus on \emph{what} system functionality is useful for an application.

Keywords: Engineering.
Electrical Engineering.
Advisor: Fan Jonathan
Fan Shanhui
Kochenderfer Mykel
Algorithms and execution traces

Part I surveys the current literature around defining algorithms. I consider two prominent approaches: the traditional approach (arising from computability theory) and the abstract approach (generalising the traditional approach). I argue that both approaches have shortcomings stemming from attempting reconcile algorithms understood as "effective procedures" with algorithms understood as "distinct methods" for solving problems. I conclude that current attempts to analyse algorithms pay too much heed to the first of these perspectives and propose giving an account of algorithms which fully embraces the second. Part II develops such an account, introducing the framework within which the remainder of the dissertation is developed. I analyse algorithms in terms of their execution trace setsm avoiding any commitment to a particular model of computation. Part II applies the framework to issues around algorithmic identity, level relativity, finiteness and determinism. Part III takes a formal turn, developing the framework from Part II with mathematical rigour. I define algorithmic trace sets, consider abstract implementation, and reconcile the trace set account with the effective procedures perspective by reconstructing computability theory

Keywords: Humanities and Sciences.
Philosophy.
Advisor: Icard Thomas
Etchemendy John
Wootters Mary
Benthem Johan van
Algorithms for high-performance brain-computer interfaces

Brain-computer interfaces (BCIs) are a set of technologies that enable people to interact with devices by directly translating neural activity into command signals. Noninvasive methods such as EEG and fMRI allow minimal-risk access to brain signals but are limited by low spatial and temporal resolution, respectively. By contrast, intracortical BCIs (iBCIs) benefit from unparalleled spatial and temporal resolution recordings of local neuronal ensembles, resulting in some of the highest-performance communication systems to date. While promising, several key challenges remain for wider adoption of these technologies. First, iBCIs in particular suffer from signal instability that cause performance degradation across time. A key challenge for clinical translation of these iBCI cursor systems is ensuring robust, long-term control for end users without manual retraining. Second, while these technologies have enabled point-and-click based typing interfaces approaching 8-10 words per minute, an ideal interface would match the bandwidth of conversational speech (roughly 150 words per minute). Third, intracortical systems require brain surgery, which has inherent risks due to the invasive nature of the procedure. Developing noninvasive approaches for silent speech decoding may therefore be more suitable for a subset of patients. In this work, we discuss three advances that tackle these key areas by leveraging modern machine learning methods alongside high signal-to-noise (SNR) recording systems. First, we present an unsupervised recalibration procedure for improving cursor BCI robustness. Using data from our clinical trial participant, we highlight the extent and timescales of neural feature drift. We demonstrate how existing state-of-the-art procedures are seemingly ill-equipped to deal with long-term drift using both offline data as well as simulation models. We then introduce a novel procedure that leverages task structure to recalibrate a system automatically, and demonstrate superior performance in-silico and in closed-loop in our participant. Second, we lay the groundwork for intracortical speech BCI efforts by prototyping a system in an offline manner using microelectrode arrays in dorsal motor cortex. We demonstrate that, despite being located in a nontraditional brain area for speech decoding, our arrays provide high SNR compared to existing approaches. Third, we leverage skin-like flexible electronics and deep learning for silent speech decoding from electromyography (EMG) signals

Keywords: Medicine.
Advisor: Druckmann Shaul
Gardner Justin
Linderman Scott
Sussillo David
Algorithms, statistical models, and their applications to real-world networks and choice systems

With the rise in popularity of social media and e-commerce platforms, discrete math is at the heart of our online experiences, playing a front-end role in the recommendation of what to click, watch, or buy, or who to follow or friend, as well as a back-end role in data storage, access, and transfer. This thesis focuses on two such discrete math problems -- the modeling of discrete choices, and the efficient storage of massive graphs. On the latter, we study the problem of balanced graph partitioning, a problem of paramount importance in domains such as graph clustering, community detection, distributed graph computation, and also efficient data storage. Chapter 2 introduces a taxonomy of existing algorithms for finding a balanced partitioning of an input graph, and pioneers a novel class of prioritized restreaming algorithms, showcasing their superior performance in minimizing the edge-cut objective over industry state-of-the-art methods. We investigate the former, the statistical modeling of choices and rankings, in the context of social choice, eg. school choice and ranked-choice-voting. Such models are used for explanation of user behaviors, forecasting demand, or counterfactual analysis. Chapter 3 unveils the significance of within-agent taste-heterogeneity in the assembly of ranked preference lists, giving rise to the concept of rank-heterogeneous preference models. Applied to real-world school-choice data, these models demonstrate enhanced predictive accuracy and a better fit to observed ranking patterns than commonly-used rank-homogeneous strategies. Chapter 4 further extends the frontier by drawing attention to an under-studied task in the ranking modeling literature -- the modeling of partial orders. Specifically, this task is concerned with the development of models whose sample space is the space of orderings of any length of n items, a space that subsumes S_n. The work develops two classes of models, composite and augmented, and demonstrates their potential for simultaneously capturing preferences and length distributions within real-world partial ranking data from school choice and ranked-choice voting contexts.

Keywords: Engineering.
Advisor: Ugander Johan
Ashlagi Itai
Lo Irene (Management science professor)
Aligning law, policy, and machine learning for responsible real-world deployments

Recent advances in machine learning (ML) have seen systems achieve human-like performance in specific tasks, especially in the realms of sequential decision-making and foundation models. Foundation models, powered by Transformer architectures, offer unparalleled abilities to generalize from vast text data, while innovations in reinforcement learning have allowed for mastering of tasks from playing Go to controlling nuclear fusion reactors. Consequently, ML is swiftly integrating into critical sectors like law, healthcare, and government, promising potential public benefits but simultaneously introducing intricate ethical, legal, and policy challenges. As ML deployments grow in real-world scenarios, exploring their interplay with laws, policies, and societal norms becomes paramount. This thesis seeks to address this nexus of ML and legal-policy environments. It endeavors to align AI's potential for public good while ensuring its safe and equitable deployment by bringing together the fields of AI, law, and policy. The first part of this thesis explores machine learning for public benefit. First, it investigates the application of foundation models in legal contexts, examining their strengths and potential pitfalls. Second, it delves into the challenges and opportunities of employing sequential decision-making algorithms in law and public policy, highlighting the unique methodological challenges posed by these domains---as well as potential opportunities for positive impact. Finally, it presents an in-depth case study from a collaboration with the Internal Revenue Service (IRS), introducing the optimize-and-estimate structured bandits problem and offering novel solutions with profound implications for tax compliance and revenue generation. However, realizing ML's societal potential necessitates stringent safeguards, which the second part of the thesis examines. First, it provides an in-depth study of the alignment of foundation models data filtering rules with human-crafted norms in legal settings, leveraging the vast body of legal decision-making as a source of rule-based alignment of machine learning filtering rules. Second, it discusses the dual-use nature of foundation models and introduces a novel approach of task blocking, producing models that are inherently resistant to repurposing for potentially harmful tasks. This work pioneers an interdisciplinary roadmap, blending ML research with legal and policy insights with the aim of fostering responsible real-world machine learning deployments for public good.

Keywords: Engineering.
Advisor: Jurafsky Dan
Ho Daniel E.
Liang Percy