VictorJuiz/thesis huggingface dataset:
Data-Centric Machine Learning for Human-Centric Applications

Climate change and pandemics are two of the most pressing threats facing humanity today. Addressing these urgent threats require immediate mitigative actions. In the US, buildings are responsible for 40% of primary energy consumption, 73% of electrical use and 40% of greenhouse gas emissions, the primary cause of global warming, and such high levels are now rapidly spreading across the rest of the world. At the same time, buildings are integral to human lives, as we spend most of our time in them which substantially affects our health and productivity. So, for climate change mitigation, it is essential to optimize energy use in buildings while ensuring human comfort. On the other hand, for pandemics mitigation, it is crucial to diagnose and have a better understanding of the new disease in a time-sensitive manner. Over the years, Machine Learning (ML) as a tool has been widely utilized for both the above efforts. However, both buildings and pandemic-specific healthcare systems exhibit a number of shared data-specific challenges, hindering robust ML implementations./nWe will present 3 major research works on tackling them with generative modeling, and transfer learning. The first work will be on conditional synthetic data generation, where the focus is to conditionally generate synthetic data for classes with infrequent data points. The applications include tackling class imbalance in healthcare data, and privacy-preserving data sharing. The second will be on improved pre-processing methods for tabular data (a common data type in smart buildings) to enable seamless use by many ML algorithms. To improve the generalizability and scalability of the models, the third work will be on a transfer learning-based adversarial domain adaptation method, with applications in adapting personal thermal comfort models in buildings from one occupant to another without using any data labels for the target occupant. With this method, the time and the resource-intensive task of acquiring multiple labels for the target environment in a building can be avoided.

Advisor: Spanos, Costas J.
Models of interacting photosynthetic light harvesting complexes constructed from modified tobacco mosaic virus capsid proteins

Energy transfer in photosynthesis is orchestrated via dynamic networks of pigment arrays embedded within protein complexes that often span a lipid membrane. This process has been imitated in models that seek both to elucidate the structural characteristics leading to efficient energy transfer in photosynthetic organisms and to use these principles to design more efficient or "greener" solar technology. Pigment conjugates of assemblies of the tobacco mosaic virus capsid protein (TMVCP) have been extensively used as model light harvesting systems due to their synthetic tractability and structural similarity to many photosynthetic light harvesting complexes. This dissertation demonstrates how changes to theTMVCP sequence, including permutation and point mutations, affect the assembly state structure of TMVCP. In doing so, the utility of charge detection mass spectrometry for the characterization of heterogeneous assemblies of large protein complexes is demonstrated. In addition, several methods for dual protein functionalization are developed, which expand the TMVCP light harvesting model to capture interactions between adjacent light harvesting complexes. A site-selective, asymmetric, protein–protein conjugation using two expressed non-canonical amino acids and an oxidative coupling reaction is used to covalently link an engineered TMVCP assembly containing donor pigment arrays to an engineered TMVCP assembly containing acceptor pigment arrays. This model demonstrates directional energy transfer from the donor to acceptor complexes with 21% efficiency, measured by a decrease in donor fluorescence lifetime when donor complexes are in the presence of acceptor complexes versus donor complexes alone. Separately, an engineered TMVCP assembly is site-selectively labeled with a donor pigment, followed by labeling with a non-expressed His tag at an engineered cysteine residue catalyzed by the enzyme tyrosinase. This His tag is associated with a supported lipid bilayer, and the TMVCP light harvesting complexes associated with bilayers demonstrate lateral mobility, imitating the movement of natural photosynthetic light harvesting complexes within phospholipid bilayers. This is the first example of a photosynthetic membrane mimic using entirely synthetic protein, lipid, and pigment components.

Advisor: Francis, Matthew B
Understanding correlated insulating ground states of magic-angle twisted bilayer graphene

When two layers of graphene are put on top of each another with a relative twist, their lattice mismatch gives rise to a moir\'{e} pattern. When the twist angle is near 1.1 degrees, a so-called magic angle, the band structure of twisted bilayer graphene becomes extremely flat around the Fermi energy, enhancing the effect of electron-electron interaction. Remarkably, magic-angle twisted bilayer graphene (MATBG) becomes superconducting at low temperatures. The origin of this superconducting behavior remains elusive./nCuriously, the superconducting behavior is usually accompanied by correlated insulating behavior in nearby parameter regions in the phase diagram. These correlated insulators cannot be described by non-interacting band theory, hinting at the importance of electron-electron interaction./nIn this thesis, we attempt to understand these correlated insulators by a combination of numerical and analytical techniques. On the numerical side, we will utilize the density-matrix renormalization group (DMRG) extensively to obtain the ground states of MATBG. Owing to the complexity of the problem, this required us to develop a non-trivial routine for encoding the Hamiltonian. We find that DMRG often reproduces the findings from Hartree-Fock simulations.On the analytical side, we aim to understand different symmetry-breaking states of MATBG. Based on symmetry analysis, we propose using scanning tunneling microscopy (STM) to distinguish between different candidate ground states, and test our analytical prediction using numerical simulation./nTaken together, this thesis represents a significant step toward understanding the correlated insulating behavior of MATBG.

Advisor: Zaletel, Michael
Rapid Adaptation for Robot Control

Once robots are deployed in the real world, they will inevitably encounter scenarios that they have never seen before. Consequently, to develop robots that can help us in routine tasks, we need to first make progress towards the problem of generalization in robotics, which asks the question: how do we get robots to handle scenarios beyond what they have seen before? This thesis will present an approach to achieve such generalization through the use of rapid adaptation. Concretely, the proposed algorithm - Rapid Motor Adaptation (RMA) - allows robots to generalize by adapting to new and unseen scenarios in fractions of a second. The proposed algorithm is developed in the context of blind quadruped walking in complex terrain in the real world. Subsequently, it is extended to quadruped walking with egocentric vision, enabling robots to cross very challenging terrains, such as stepping stones, which are beyond the reach of blind systems. The same algorithm is then applied to the task of in-hand rotation to develop a single controller capable of rotating a diverse set of objects in the real world, as well as to bipedal robots.

Advisor: Malik, Jitendra
Structure-Aware Representation Learning and Its Application to Medical Imaging

Deep learning models have made remarkable progress in understanding object-centric visual data by focusing on one or more objects present in the scene. However, they may not perform well for scene-centric visual data that includes multiple objects, backgrounds, and other elements in the scene. Furthermore, the low interpretability of deep learning models due to their high complexity may hinder their trustworthiness and practical deployment in the real world./nIn this dissertation, we present a structure-aware representation learning method to address these challenges. Rather than solely relying on end-to-end supervised learning, we first use deep learning to learn multi-level representations that explicitly model the structures of individual objects, such as their shape and geometry, and then map the representations to the final prediction with a shallow classifier like a single-layer perceptron. This approach achieves more robust performance for non-object-centric visual data as it explicitly extracts structure-aware representations from the input. Additionally, the method disentangles representation learning and classification. Thus, analyzing the shallow classifier can provide quantitative interpretation of why a prediction is made. We demonstrate the effectiveness of our approach on several standard computer vision benchmark datasets, as well as real-world medical applications like dry eye disease diagnosis, and 3D dental casting./nPart I of the dissertation describes a segmentation-based method to learn instance-level representations that enable us to understand the individual characteristics of objects. We demonstrate its effectiveness with an application to multi-level gland morphology quantification from medical images for disease diagnosis purposes./nPart II presents approaches for learning geometric shape representations from visual data and how such representations can be used for reconstructing 3D shapes. We provide a medical application of 3D dental casting and jaw reconstruction./nIn Part III, based on the instance-level and shape-aware representations from previous parts, we map the representations to the final prediction with a shallow model and show how it could be analyzed and interpreted. We demonstrate an application to demographics prediction from medical images, where we can identify the most relevant features that inform the model's decision and improve its reliability./nTo make the proposed method suitable for deployment for practical uses, Part IV introduces how we improve the efficiency of deep learning models using constrained neural optimization. We provide its special cases including orthogonal convolutional neural networks and recurrent parameter generators.

Advisor: Yu, Stella
Lin, Meng
Does How You Feel Depend on Who You Are? The Moderating Role of Personality on Emotional Context Effects

There is a plethora of literature linking Extraversion to the experience of positive emotions and Neuroticism to the experience of negative emotions. Further, it has been argued that these relationships have important consequences for well-being. In addition to these main effects of the trait, research on person-situation interactions has shown that individuals have differential reactivity to emotional situations, even identifying some direct causal links using experiments, based on their underlying Extraversion and Neuroticism. However, much less is known about how this differential reactivity might generalize outside of the lab, to naturalistic situations. In Study 1, we test this claim by capitalizing on the natural lockdown that occurred as a result of the COVID-19 pandemic, asking how emotion experience changed due to this lockdown, and whether these changes were moderated by Extraversion, Neuroticism, and the less studied but important trait of Agreeableness. Further, we add an investigation of these potential interactions using facet-level personality. These constructs represent a more specific level of personality analysis than personality traits and have received almost no attention in the literature on the relationships between personality and emotion. However, this increased specificity allows for important clarifying hypotheses about the relationships between personality and emotion to be tested, such as whether the associations between Extraversion and positive emotions are due more to social contact or behavioral activation. In Study 1, we showed that individuals did respond differently to the lockdown based on their underlying Extraversion, Neuroticism and (to a lesser extent) Agreeableness. Further, we showed that the relationship between Extraversion and positive emotions is likely due to behavioral activation more so, or even in place of, social contact. In Study 2, we ask whether these results generalize to a more traditional in lab emotional situation manipulation, using a sad film clip. We capitalize on modern statistical techniques, namely multilevel modelling, to advance the existing work in this area and show that both our results from Study 1, as well as the findings from previous work, which made use of difference scores as dependent variables instead of multilevel models, both generalized well. We discuss the implications of these findings for personality theory, emotion theory, and person by situation interactions, as well as highlight some suggestions for future research.

Advisor: John, Oliver P
Rethinking System Design for Expressive Cryptography

Expressive cryptography, including Secure Multi-Party Computation (SMPC), Fully Homomorphic Encryption (FHE), and policy-based encryption, has the potential to enable transformative new applications. Unfortunately, it is often slow and resource-intensive, making those applications difficult to realize. For example, SMPC enables multiple organizations (e.g., hospitals) to run joint computations on their data (e.g., for better medical diagnosis and treatment) while keeping the inputs to the computation (e.g., patient data) secret. But SMPC can have high memory overhead, making it difficult to scale such applications to large problem sizes. As a result, while expressive cryptography has seen some notable real-world usage, such as Meta using SMPC in its advertising business, existing adoption is not widespread, limited to incipient and isolated deployments./nThis dissertation studies how to design and build networked systems to enable expressive cryptography to reach its full transformative potential. We present six system design techniques for systems relating to expressive cryptography, classified into two high-level approaches. We validate our techniques by using them to design and implement four systems: MAGE, TCPlp, JEDI, and Ghostor./nOur first high-level approach is to make expressive cryptography generically more efficient by redesigning the underlying systems that expressive cryptography uses. For example, MAGE provides virtual memory for SMPC and FHE at nearly zero cost, allowing them to efficiently scale beyond the available memory to larger problem sizes. TCPlp is a performant TCP-based transport layer for low-power wireless networks, which allows the large ciphertexts and signatures associated with expressive cryptography to be efficiently transferred over the network./nOur second high-level approach approach is to make expressive cryptography practical for particular applications by rethinking how and when to use expressive cryptography. For example, we designed Ghostor, a data-sharing system, and JEDI, an end-to-end encryption protocol for publish-subscribe IoT deployments, using this approach. Ghostor uses a blockchain and JEDI leverages policy-based encryption, but they are carefully designed to use these components rarely and outside of the critical path of user-facing operations./nWe further validate our techniques by using them to analyze related work, to identify existing work that applies our techniques and opportunities to improve existing systems using our techniques. Then, we discuss the impact of our work, including the adoption of TCPlp as the TCP implementation in OpenThread, an open-source network stack used in the smart home IoT industry, including by Amazon Eero and Google Nest. We hope that our techniques, and the systems we designed using them, will accelerate the widespread adoption of expressive cryptography, bringing stronger security to existing applications and enabling exciting new ones.

Advisor: Culler, David E.
Popa, Raluca A.
Virtual Diabetes and Hypertension Care in Community Health Centers: Use, Quality, and Patient Preferences

In response to the COVID-19 pandemic, telehealth was rapidly implemented to make remote care possible. While pre-pandemic levels of telehealth utilization were low generally, community health centers (CHCs) had particularly low adoption due to policy barriers and limited access for low-income populations. One major challenge in understanding telehealth utilization among patients of CHCs is disentangling patient preferences and barriers to use. For instance, low-income populations may prefer in-person care, but this option may be constrained by structural barriers, including employer flexibility for time off and financial considerations. Despite lingering volatility in the policy landscape, unclear patient preferences, and unique barriers to implementation, many CHCs accelerated telehealth implementation./nIn order to address barriers relevant to CHCs, this dissertation examines factors that impact telehealth implementation. Chapter 1 is a systematic review conducted to understand factors that influence organizational adoption of telehealth in safety net settings and how to overcome barriers. Chapter 2 examines the association of care continuity with telehealth use and quality of diabetes and hypertension care in CHCs before and during the COVID-19 pandemic. Finally, Chapter 3, utilizes a conjoint experiment with a latent class analysis to unpack preferences and needs of underserved patients that impact patient adoption and acceptance of telehealth. The three analyses together advance evidence about successful telehealth implementation for underserved patient populations.

Advisor: Rodriguez, Hector P
Sounding Civilization: Race and Sovereignty in the Imperial Music of Siam

My dissertation accounts for the sonic history of an absolute monarchy—the kingdom of Siam under Chulalongkorn—in the decades around 1900. In the wake of its defeat in the Franco-Siamese War of 1893, Siam was in crisis over the racial and civilizational standing of its regional empire against encroaching European colonization across Southeast Asia. This study examines the way the monarchy employed listening, criticism, and emulation of European musical practices to assert sovereignty and to negotiate colonial strategies of racialization. The musical reforms cultivated at court portrayed the Siamese elite as cosmopolitan equals to courtiers of the great monarchies of the world. At the same time, the changes in musical thought and practice at court magnified the ethnic difference between Bangkok as a royal center (the locus of the cosmic glory of the king) and Siam’s own rural peripheries (the realm of “lesser” ethnicities vulnerable to French and British takeover). I also examine the strategies by which the monarchy fashioned itself as interimperial listeners – listening from the situated space between empires as judges of both Siamese and European music practices – and as colonial intellectuals eager to adopt and harness, rather than resist, European music theoretical discourses of racial hierarchy./nEach of the four chapters focuses on a different mode of musical reform enacted as efforts of racialized self-invention that balanced between satisfying traditional Siamese cosmology and European colonial approval. These reforms in musical practice and thought, routed through the underlying rhetoric of race and civilization, illustrate from the Siamese perspective the commensurability of sonic spectacle as an index of civilizational and racial excellence in situations of colonial contest. While the music history addressed here centers Siam’s actions in response to European colonialism, I stress that these practices were not enacted as subaltern efforts of anticolonial resistance, but as a waning monarchy’s bid to wrest the markers of sovereign power in a newfound global-colonial order.

Advisor: Smart, Mary Ann
Isolated Photon Cross Sections and Nuclear Modification Factor in √ sNN = 5.02 TeV pp and p–Pb with ALICE

Prompt photons, produced via QCD Compton scattering or quark and anti-quark annihilation at leading order, are unique probes to study QCD processes since they do not interact strongly and therefore allow to control the dynamics of the initial hard parton scattering. Isolated photon production in pp collisions is one of the most clear tests of perturbative QCD processes and parton distribution functions. Photon measurement in p–A collisions provides the opportunity to measure possible modifications of the nucleon structure function in nuclei. ALICE has measured isolated photons at low pT, thus extending previous measurements down to small x. In this thesis, the cross section of isolated photons for the range 12 GeV/c < pT < 60 GeV/c is presented in pp and p–Pb collisions at √sNN = 5.02 TeV along with photon nuclear modification factor (RpPb).

Advisor: Jacak, Barbara
The Political-Economic Explanation for the US–South Korea Alliance’s Cohesion

This dissertation investigates the dynamics of cohesion within the US–South Korea alliance, along-lasting but asymmetric defense partnership, and aims to answer the question: What factors drive changes in the alliance’s cohesion? While enduring alliances are often viewed as cohesive, this study demonstrates that changes in cohesion can occur even within long-lasting alliances, necessitating an examination of cohesion dynamics separate from alliance durability. Two main arguments are presented. First, changes in the US–South Korea alliance’s cohesion were influenced by the interplay of international and domestic political-economic factors. The study argues that, in some cases, domestic constraints may outweigh international factors like shared threats and strategic interests in shaping the alliance cohesion. Second, in asymmetric alliances, the strategic interests and political-economic situation of the major power (the US) had a more significant impact on the alliance cohesion than the minor power (South Korea). Furthermore, the strategic interests and political-economic situation of the US, a major power, influenced not only its alliance policy but also the alliance policy of South Korea, a minor power closely monitoring the US situation. An in-depth historical case study of the US–South Korea alliance during the 1960s and 1970s is conducted, a salient period characterized by significant changes in the alliance’s cohesion. I select this period because it effectively demonstrates the inadequacy of existing literature focusing on threats and interests and validates the main arguments of this study. The study analyzes how evolving threat perceptions, strategic interests, and domestic political-economic developments contributed to changes in the alliance cohesion. It offers valuable insights into the driving forces behind these changes and the complex interplay among them, ultimately enriching our understanding of alliance dynamics. This study contributes to the existing research on the US–South Korea alliance and holds potential for further expansion into a general theory of alliance cohesion in asymmetric contexts.

Advisor: Sargent, Daniel J.
Nacht, Michael A.