VictorJuiz/thesis huggingface dataset:
Machine Learning for Query Optimization

Data has been growing at an unprecedented rate in the past two decades. As a result, systems that store, process, and analyze data have become mission-critical. Crucial to the performance of data systems is the query optimizer, which translates high-level declarative queries (e.g., SQL) into efficient execution plans. However, query optimization is highly complex, leading to two key challenges. First, optimizers use a myriad of hand-designed heuristics to tame the complexity, but heuristics leave performance on the table. Second, optimizers are highly costly to develop, where human experts may spend months writing a first version and years refining it./nThis dissertation applies and enhances machine learning advances to tame the complexity in query optimization. First, we remove for the first time decades-old and accuracy-impacting heuristics in cardinality estimation—the Achilles’ heel of optimizers where heuristics particularly abound—thereby significantly improving estimation accuracy. We present Naru and NeuroCard, two cardinality estimators based on self-supervised learning advances that learn the joint data distribution of tables without any heuristic assumptions. Our estimators improve the accuracy of cardinality estimation by orders of magnitude compared to the prior state of the art. Second, we show that automatically learning to optimize SQL queries, without learning from an expert-designed optimizer, is both possible and efficient, thereby potentially alleviating the high development cost. We introduce Balsa, a deep reinforcement learning agent that automatically learns to optimize SQL queries by trial-and-error. Balsa can learn to outperform the optimizers of PostgreSQL—one of the most popular database systems—and a commercial database engine with a few hours of learning./nOverall, by enhancing machine learning advances with new, carefully designed systems and ML techniques, this line of work improves existing query optimizers, while opening the possibility of alleviating the complex optimization in future environments and engines.

Advisor: Stoica, Ion
Snapshot Spectroscopic Studies of the In Vivo Regulation of Photosynthetic Light Harvesting and Photoprotection

As the global population continues to grow, optimizing photosynthetic light harvesting has emerged as an important route for increasing the production of food and bioenergy. Under periods of excess illumination, photosynthetic organisms initiate a suite of photoprotective mechanisms, collectively referred to as nonphotochemical quenching (NPQ), which thermally dissipate chlorophyll excited states. However, the molecular basis for NPQ is still unclear, partly due to overlapping dissipation mechanisms, shared molecular players, significant variation across species, and the difficulty of experimentally measuring dissipation in vivo and under physiologically relevant conditions. Additionally, while NPQ is thought to protect against downstream oxidative damage, there has been little direct investigation of the temporal and spatial connections between NPQ and reactive oxygen species, such as singlet oxygen, which is thought to be a toxic byproduct of photosynthesis though it may also play essential roles in intra-cellular signaling./nIn this dissertation, I employ a suite of “snapshot” spectroscopic methods – including transient absorption, fluorescence lifetime, and electron paramagnetic resonance techniques – in order to better understand the complex operation of NPQ in intact photosynthetic systems. By applying these snapshot methods to a range of genetically altered plants and algae under dynamically changing illumination conditions, a more complete understanding of the complex in vivo operation of excitation energy dissipation is obtained, with particular emphasis on the roles of carotenoids, pH-sensing proteins, and oxidative species. These fundamental findings hold great promise for ongoing efforts to boost the biomass yields of natural photosynthesis as well as improve the sustainable production of bioenergy.

Advisor: Fleming, Graham R
Multi-level Politics and Central European Democratic Development

Democratic backsliding, illiberalism, and authoritarian encroachment are undermining the political stability of many newly democratized countries. This dissertation examines challenges to liberal democracy in Central Europe, theorizing and testing how subnational and supranational tiers of government contribute to democratic backsliding. In the 1990s empowering subnational governments and the EU was seen as an essential strategy for promoting the liberal democratic transition. Instead, we have seen these levels of government have facilitated democratic backsliding as the multilevel character of policymaking and politics and has become intertwined with democratic decline in the region./nThe chapters in this dissertation use original data to empirically analyze the democratic backsliding process in Central Europe at multiple levels. Chapters 1 and 2 examine local backsliding; Chapter 1 focuses on party strategy in subnational electoral politics Hungary, where Fidesz has pushed democratic erosion furthest in the region. Chapter 2 examines differences in electoral rules in Hungary and Poland to understand why local politics have been more resilient in Poland than in Hungary. Chapter 3 shifts focus to the supranational level, to understand how democratic oversight in the European Parliament remains beholden to national-level dynamics, limiting its ability to curb the democratic backsliding processes. Together, these chapters show that the multilevel politics of the region have been an important component of democratic decline.

Advisor: Ansell, Christopher K.
Explaining for the Best Intervention

Humans don't grow a mind by sitting in an armchair and reading flashcards containing world facts. Much of human knowledge comes from experimentation. For instance, do antidepressants affect both mood and thoughts directly, or do they affect thoughts via mood? To test competing hypotheses, one can intervene on a variable (e.g., changing mood using another method) to see how it affects other variables. Good interventions generate information that discriminates between hypotheses. However, informative interventions are hard to design. In many past studies, explaining why something occurs is used as a simple but powerful tool to help learners acquire generalizable abstractions useful for future scenarios. In this dissertation, I investigate whether asking causal learners to explain why they plan to carry out certain interventions helps them select more informative interventions./nChapter 1 describes the task used throughout the dissertation: Three light bulbs are connected one way or another; learners intervene on one light bulb to find out their true structure. The optimal intervention maximizes the expected information gain (EIG) by generating distinct outcomes under different structures. The suboptimal positive test strategy (PTS) tests one hypothesis at a time and favors the intervention that can potentially affect the highest proportion of hypothesized connections. A Bayesian model captures how much a learner relies on EIG vs. PTS to choose interventions./nIn Chapter 2 (Study 1), I examine intervention strategies that adults and 5- to 7-year-olds naturally use to select interventions in the Light Bulb Game described above. Adults mainly relied on the optimal strategy, EIG maximization, whereas children mostly used PTS. Following informative interventions, adults identified the correct structures most of the time, but children were at chance./nIn Chapter 3 (Study 2), I prompt adults and 5- to 7-year-olds to explain their intervention choices ("Why do you wanna turn on X light bulb?'') and examine if it changes their intervention strategies. Explainers did not intervene differently. However, children who either explained or reported their choices performed above chance at identifying true causal structures from intervention outcomes./nIn Chapter 4 (Study 3), I train 7- to 11-year-olds on the difference-making principle which underlies EIG maximization: That a light bulb is helpful if it makes different things happen in different structures, and unhelpful if it leads to the same outcome either way. Training led children to rely more on EIG. The effect was more pronounced in 9- to 11-year-olds than it was in 7- to 8-year-olds./nIn Chapter 5, I synthesize findings in Studies 1--3 and propose directions for future research.

Advisor: Xu, Fei FX
Cognitive analyses of machine learning systems

Machine learning systems are increasingly a part of human lives, and so it is increasingly important to understand the similarities and differences between human intelligence and machine intelligence. However, as machine learning systems are applied to more complex problem settings, understanding them becomes more challenging, and their performance, correctness, and reliability become increasingly difficult to guarantee. Moreover, "human-level performance" in such settings is often itself not well-defined, as many of the cognitive mechanisms underlying human behavior remain opaque. This dissertation bridges gaps in our understanding of human and machine intelligence using cross-disciplinary insights from cognitive science and machine learning./nFirst, I develop two frameworks that borrow methodologically from cognitive science to identify deviations in the expected behavior of machine learning systems. Second, I forge a connection between a classical approach to building computational models of human cognition, hierarchical modeling, and a recent technique for small-sample learning in machine learning, meta-learning. I use this connection to develop algorithmic improvements to machine learning systems on established benchmarks and in new settings that highlight their inability to come close to human standards. Finally, I argue that machine learning should borrow methodologically from cognitive science, as both are now tasked with studying opaque learning and decision-making systems. I use this perspective to construct a computational model of machine learning systems that allows us to formalize and test hypotheses about how these systems operate.

Advisor: Jordan, Michael I
Noise Tailoring for Enhancing the Capabilities of Quantum Computers

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. However, qubits in the noisy intermediate-scale quantum (NISQ) era are short-lived and susceptible to a variety of errors and noise due to imperfect control signals and incomplete isolation from the surrounding environment. For example, systematic miscalibrations, unwanted entanglement, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors can severely limit the performance of quantum algorithms in an unpredictable manner on timescales shorter than the coherence times of qubits. In recent years, there has been growing interest in using methods which randomize the physical implementation of quantum gates to mitigate the impact of coherent errors, effectively tailoring them into a form of stochastic noise. In this thesis, we study one such method --- randomized compiling --- and show how gate errors under randomized compiling are accurately described by a stochastic Pauli noise model without coherent errors. We demonstrate significant performance gains under randomized compiling for various different quantum algorithms, such as the quantum Fourier transform. We further show that randomized compiling can improve the predictability of quantum algorithms, and enables unique forms of error mitigation for enhancing the performance of quantum computations in the NISQ era. Finally, we show that randomized compiling can reduce worst-case error rates by orders of magnitude, enabling the accurate characterization of quantum gates for fault tolerance. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day, noisy quantum processors, paving the way forward for scalable quantum computing and fault tolerant quantum error correction.

Advisor: Siddiqi, Irfan
An Activity-Based Oxaziridine Platform for Identifying and Developing Covalent Ligands for Functional Allosteric Methionine Sites

The rational design of compounds to covalently label natural amino acids has become a popular method in both therapeutics design and chemical biology exploration. The field is saturated with rich chemistry for the targeting of nucleophilic amino acids, with chemical advances in recent years opening the door to the targeting of additional amino acids. To further expand the realm of targetable amino acids, we have developed and assessed oxaziridines as covalent methionine-targeting reagents via Redox-Activated Chemical Tagging (ReACT) chemistry. This thesis describes work on establishing a platform for covalent methionine screening using cyclin-dependent kinase 4 (CDK4) as a model protein. Additionally, this thesis explores efforts at improving equity and inclusion within the College of Chemistry at UC Berkeley via a graduate student-led mentorship program aimed at improving the number of undergraduate transfer students in research labs as a method for mitigating the effects of transfer shock and lessening barriers to advanced education in STEM.

Advisor: Chang, Christopher J
Building Cryptographic Systems from Distributed Trust

Centralized systems are prone to data breaches, which may come from hackers and malicious or compromised employees inside the company. The scale and prevalence of such data breaches raise concerns from users, companies, and government agencies. In this dissertation, we study how to build systems that distribute centralized trust among many parties, such that the system remains secure as long as at least one of the parties is not compromised. We show how distributed trust can be used to secure systems for storage, machine learning, consensus, and authentication.

Advisor: Popa, Raluca
Integrated Multiport Conversion with Rotating Transformer

Single-phase energy systems are becoming increasingly common due to the rise of resi-dential scale (∼3kW) renewable energy in the US. Integrating storage, sources, and loads poses many challenges associated with power balancing and being resilient to power supply variations or interruptions. A multi-port hub for integrating a single-phase utility connection, DC battery storage, DC photovoltaic (PV) generation, and critical customer loads is presented in this dissertation. At the core of the system is an electric machine that functions as a multiport rotating transformer, providing voltage conversion, galvanic isolation, 120 Hz ripple energy balancing for single-phase ports, and hold-up energy by use of intrinsic stored kinetic energy in rotation. The energy stored in rotation of the machine is orders of magnitude greater than that available in the capacitor bank often used in static systems and has no associated power versus energy trade-off. A transverse flux topology was chosen for the rotating transformer due to the topology’s true torque scaling with respect to pole pairs and its simple winding configuration. The transverse flux machine was chosen to be have a U-core topology for its relative ease of manufacturing. A convex optimization design method was used to determine the machine physical dimensions and operating speed. The number of stator poles and magnets was specifically chosen to minimize cogging. A prototype system has been constructed that demonstrates all the functionalities of the rotating transformer. The prototype system validates the multiport system with rotating transformer as an effective and cost efficient method for the integration of typical residential PV components. This dissertation covers the design and implementation of the rotating transformer, its mechanical suspension, the system control, and the system power electronic converters.

Advisor: Sanders, Seth
Structural and Functional Studies of Two-Pore Domain Potassium Channels

Two-pore domain potassium channels (K2Ps) are widely expressed ion channels which generate leak-type currents regulated by diverse chemical and physical stimuli including temperature, membrane tension, signaling lipids, and pH, to control the resting membrane potential (RMP) of cells. While these K2Ps are critical to the maintenance of the RMP, the structural mechanisms by which they respond to physiological stimuli remain largely unknown. Additionally, studies of K2Ps in vivo have been hampered by the lack of molecular tools which can be used to probe K2P function and structure./nHere, we describe the structural basis for gating of the K2P TWIK1 and describe a series of molecular tools generated against the K2P TREK1. Utilizing cryo-electron microscopy (cryo-EM), we determine high resolution structure of TWIK1 in lipid nanodiscs at a high and low pH, and in high extracellular K+, and describe the mechanistic basis for pH-gating in this channel. We then describe the generation of a nanobody which specifically binds another K2P, TREK1, with nanomolar affinity. Finally, we describe the computational prediction of a series of novel small-molecule TREK1 agonists. The data and molecular tools presented here further our understanding of the structural and functional properties of the K2P family of ion channels.

Advisor: Brohawn, Stephen
Motion planning of quadcopters for enhanced autonomy in complex environments

Flight range and time, as well as autonomous flights in complex environments, are two challenges preventing quadcopters from being more widely used in the industry. This thesis reduces the effect of these two problems via several motion planning methods./nThis thesis is composed of three parts. In the first part, two methods are proposed to improve the flight time (endurance) and distance (range) of quadcopters. The first method does so by finding the optimal flight speed using extremum seeking control. The second method extends the first method by finding the optimal flight sideslip as well as speed, and adds a step size adapter to the extremum seeking controller to improve its convergence speed. Both methods do not require the power consumption modeling of the quadcopters and can thus adapt to changing payloads and disturbances./nIn the next part, we focus on the problem of fast collision avoidance flight in cluttered environments. First, we propose a computationally efficient memoryless planner for fast outdoor flights, using a depth camera for sensing obstacles and using the visual inertial odometry (VIO) for state estimation. Since the VIO may function poorly in areas with very few visual features or when the flight is overly aggressive. We then take the state estimation quality of the VIO into account during the trajectory planning. This perception-aware planner is able to guide the vehicle to areas with more VIO features and avoid overly aggressive trajectories. It improves the VIO's accuracy and reduces its failure rate, while only slightly reducing the flight speed./nFinally, an inertial navigation motion planning strategy is introduced. This state estimation method only requires the inertial measurement unit (IMU) and can be used as a backup when other methods fail. By breaking a long trajectory into multiple short "hopping trajectories'' and introducing zero velocity updates when the vehicle is stationary on the ground, the state estimation variance can be drastically reduced and can be used in closed-loop control of quadcopters.

Advisor: Mueller, Mark
Structural and functional studies of putative ion channels

Ion channels are a large and diverse family of proteins broadly implicated in human physiology and that of other organisms. The study of the molecular mechanism of ion channels benefits from both “top-down” physiological and “bottom-up” biophysical studies. Here, we use a reductionist biophysical approach, with a focus on the use of cryo-electron microscopy (cryo-EM) to study the structure and function of three putative ion channels. The first, ORF3a is a putative ion channel encoded in the SARS-CoV-2 genome. Here, we determined the first ORF3a structure, and characterized ORF3a activity through electrophysiological recordings and fluorescent imaging of reconstituted ORF3a proteoliposomes. The second, Tweety-homolog family (TTYH) proteins, previously characterized as putative volume-regulated anion channels. Through parallel functional and structural studies, our work determined that TTYHs are not pore-forming subunits of anion channels. Additionally, we discovered a previously unknown “trans-dimeric” conformation of TTYH2. The third putative ion channel is TMEM87A, previously implicated in mechanosensitive conduction of cations. Here we determine the first structure of an TMEM87 protein, and through structural and bioinformatic analysis, identify related proteins forming the basis for a new protein family. Collectively, this work has demonstrated the value of cryo-EM for structural studies of small (<100 kDa) membrane proteins, while laying the foundation for deeper mechanistic understanding of each of these different proteins.

Advisor: Brohawn, Stephen G
Towards Socially and Economically Beneficial Machine Learning

From digital platforms, automated transportation to healthcare, the rapid deployment of machine learning has in many ways changed our everyday life. However, when learning systems are deployed in the real world, they immediately face a complex social and economic context which poses feasibility constraints, drives the underlying dynamics, and influences the kinds of data that the systems can actually obtain. Optimizing a single offline objective in isolation to these contexts can lead to severe unintended consequences at deployment and hinder the improvement of social welfare that the system has the potential to bring./nIn this thesis, I summarize my research works on developing learning algorithms that incorporate such social economic contexts into the design from three aspects: (i) Learning with noisy input data; (ii) Learning with bandit-type user feedback; (iii) Learning under causal dynamics. I will situate each of these with the particular applications of machine learning on fair classification, resource allocation, auction and platform design.

Advisor: Jordan, Michael
Rigorous Guarantees for Randomized Diagonalization Algorithms

We rigorously analyze numerical methods for (approximately) computing the eigenvalues and eigenvectors of a matrix. Our main results are the following:
Unknown environment 'itemize'
Unknown environment 'itemize'/nA key ingredient in showing the first two results is a smoothed analysis of the eigenvector condition number and minimum eigenvalue gap of a matrix. In this direction we show a general result that might be of independent interest in random matrix theory: if one adds independent (absolutely continuous with respect to the Lebesgue measure) random variables of small variance to the entries of an arbitrary matrix $A \in \mathbb{C}^{n\times n}$, with high probability, the resulting matrix $A' $ will have (relatively) stable eigenvenvalues and eigenvectors.

Advisor: Srivastava, Nikhil
Voiculescu, Dan-Virgil