Topics in sparse Bayesian machine learning

2023This dissertation is devoted to addressing several challenging problems in machine learning via the Bayesian approach. These problems frequently arise in diverse fields, such as epidemiology, biomedicine, robust statistics and imaging science, and are usually high-dimensional and have certain sparsity assumptions. In this dissertation, we will focus on three important problems, which are sparse canonical correlation analysis, minimum distance estimation and inverse problems. For each problem, we will develop a new method from the Bayesian perspective to solve it effectively and efficiently, with statistical guarantees and numerical evidence

Advancing optical metrology through computational imaging techniques

2025Optical metrology, which uses light to precisely measure and characterize objects, is essential in industrial manufacturing, scientific research, and engineering design. Computational optical imaging techniques have leveraged novel optical systems, advanced algorithms, and increased computing power to accurately reconstruct and analyze the physical properties of objects, surpassing the capabilities of traditional optical metrology methods. This thesis explores the advancement of optical metrology through computational imaging techniques, focusing on the following three primary research areas.The first research focuses on semiconductor chip surface topography measurement, which plays a critical role in various semiconductor industrial applications. Conventional optical metrology techniques often face challenges in balancing field-of-view(FOV) and spatial resolution. To overcome these limitations, I propose a novel technique termed Fourier Ptychographic Topography (FPT). FPT provides wide FOV, high resolution, nanoscale height reconstruction accuracy. Experimental validation on a sample semiconductor chip also demonstrates the robustness and superior performance of FPT compared to current standard optical profilometry methods. In the second study, I extend my research from surface metrology to large-scale 3D particle detection from a single hologram. Traditional 3D particle localization methods often rely on single-scattering models, which are limited in their ability to accurately capture dense particle distributions at deep imaging depths. To address this limitation, I use a multiple-scattering beam propagation method (BPM) to model multiple scattering effect of 3D particle fields. I achieve robust detection of dense 3D particles and experimental results demonstrate that my approach provides significantly higher accuracy compared to conventional single-scattering models, particularly in scenarios with high particle densities and deep imaging depths. Finally, I explore the application of deep learning methods for phase retrieval, a fundamental problem in computational imaging-based optical metrology. My previous research utilized physical models to simulate the optical imaging process. While these traditional model-based methods achieved state-of-the-art results in many applications, the development of deep learning methods introduces new possibilities for solving highly ill-posed problems that were previously unsolvable. In my third study, I introduce the Neural Phase Retrieval (NeuPh) network, which enables flexible object representations and resolution-enhanced phase reconstruction from multiplexed Fourier ptychographic microscopy (FPM). Experimental results demonstrate the NeuPh network’s scalability, robustness, accuracy, and generalizability in solving the highly ill-posed phase retrieval problem, outperforming existing methods and offering potential wide applications in optical metrology techniques. Through these investigations, this thesis advances optical metrology by integrating computational imaging techniques, offering improved measurement accuracy, scalability, robustness, and paving the way for enhanced optical metrology methodologies in various applications

Conservation impact assessment and SAR forest cover mapping in the Colombian Andes

2024This thesis addresses research questions from two distinct yet complementary fields—conservation science and remote sensing—through a case study in the Colombian Andes. In Chapter One, we explore the impact on forest cover of the largest and longest-running public land acquisition (PLA) program in the tropics between 2000 and 2021. Using matching and Difference-in-Differences with multiple time periods we find that as of 2021 there has been a 3.5% increase in forest cover on protected parcels and that impact increases for at least 10 to 12 years post-treatment. We also find that impact varies significantly by factors like slope, accessibility, and department. In Chapter Two, we attempt to improve the forest cover data used in Chapter One by integrating synthetic aperture radar (SAR) observations from Sentinel-1 for areas where persistent cloud cover precludes the use of optical data. We find encouraging evidence to suggest that SAR data can be used with the continuous change detection and classification algorithm to detect forest change in topographically-complex regions, but conclude that accuracy improvements and widespread workflow adoption are dependent on the accessibility of high resolution digital elevation models and improved radiometric terrain correction for Google Earth Engine

Novel reaction discovery with rapid high-throughput experimentation via infrared spectroscopy and enzymatic electrochemical oxidation of alcohols

2024Scientists in academia as well as pharmaceutical companies have been devoting numerous research efforts towards green chemistry since the establishment of the Pollution Prevention Act of 1990. The overarching goal of green chemistry is to incorporate environmentally benign reaction design into routine and novel synthesis in order to minimize the use of toxic substances and waste while maximizing the overall efficiency of reaction processes. Both projects reported here encompass the idea of sustainability and productivity. High-throughput screening (HTS) and high-throughput experimentation (HTE) has been widely used in the field of biochemistry to rapidly identify pharmacological targets. However, this method has been underused in organic chemistry due to the complexity of reaction design, strain in reagent handling, as well as the difficulty of data analysis and structure determination. Utilization of HTE in organic chemistry can increase the output of chemists and decrease wastes. A well-designed chemical HTE platform has the potential to generate enormous volumes of empirical standardized data that not only could be used for reaction optimization, but also readily integrated into a near-AI computational system in the future for reaction model prediction and discovery. A functional screening and analysis platform was developed to investigate the Petasis Borono-mannich reaction space and to explore possible novel reactivities. Using this system, more than 3000 distinctive microscale reactions were processed in under 6 months with 74% accuracy. By triaging the IR results, we prioritized time and efforts on analyzing and reproducing reactions with moderate to excellent yields. This helped to identify previously unknown reactivities, allowing for reproduction of novel reactions at bench scales, as well as furthering mechanistic studies of uncommon reaction partners such as thiophenol and pyrone. This preliminary success suggests that this platform could be transferable to other multicomponent reactions such as the Suzuki cross-couplings and Buchwald-Hartwig aminations. With deepened understanding of reagent selection and stereoselectivity of the catalyzed Petasis reactions from the HTS platform, we examined the possibility of electrochemical enzymatic catalysis. Galactose oxidase is known to selectively eliminate the pro-S hydrogen during oxidation of alcohol reagents due to the steric constraints of the active site. Electrochemistry tend to feature mild reaction conditions and fast turn over, while enzymatic catalysis is economical and nontoxic. By combining these two methods, a mild, diastereoselective oxidation could be achieved to provide the aldehyde component in the highly enantioselective, transition-metal-free Petasis reaction

Advanced quantum structures for infrared detectors

2025Type-II superlattices (T2SLs) have emerged as promising alternatives to the more established bulk material systems for infrared (IR) photodetection. This is due to predicted fundamental advantages, such as the tunability of the band gap and theoretically reduced Auger recombination rates. However, the superiority of these materials has not been experimentally realized, prompting the need for further investigation. A bottleneck in the development of improved superlattice (SL) structures and devices is the cost in time and resources required to prototype and characterize these materials as well as incomplete knowledge of the material properties and physical phenomena that characterize these structures. Therefore, the field would greatly benefit from simulation methodologies that enable the development of advanced T2SL materials. In this work, the field of IR photodetection is reviewed highlighting the most common T2SLs structures currently being experimentally implemented. A quantum transport model that includes the necessary physical mechanisms to model carrier transport in these structures will be presented. The results of an investigation on the extraction of vertical carrier mobility, a property important for carrier collection, from quantum transport simulations is presented for an example T2SL. It is demonstrated thatcarrier transport in these structures can be highly coherent. In this case, the apparent mo-bility is suppressed due to ballistic resistance, requiring care when predicting the intrinsic mobility of these materials. The best method of mobility extraction is one that considers the dependence of the resistance on device length. This method was applied to predict the quantum efficiency (QE) in curved focal-plane arrays composed of n-type mid-wave InAs/InAsSb and InAs/GaSb structures subjected to the effects of superlattice disorder and external strain imposed by the curving procedure. It is demonstrated that the external strain has a minimal impact on the QE relative to disorder in both structures suggesting the device design could be viable. Additionally, it was found that large magnitudes of positive axisymmetric strain could result in enhanced hole transport. Finally, a comprehensive investigation is presented that probed for optimized n-type long-wave InAsSb/InAsSb SL structures, a material known to result in low QE devices, for various substrate lattice constants. Several structures were found demonstrating hole mobilities with greater resilience to SL disorder providing potential candidates for future prototyping

Three Papers on the Politics of Financial Cooperation and Statecraft

2023Financial globalization has increased interdependence among financial markets in different regions, requiring new frameworks of analysis of the politics of interstate relations to fully understand global financial markets. The three papers in this dissertation manuscript address this demand by formulating multiple hypotheses on what drives financial cooperation between states, how states use asymmetries in interdependence for statecraft, and how monetary policies of one economy can influence the politics of others. The first paper argues that financial cooperation in the international monetary system has a hub-and-spokes structure, with the United States as the hub economy. It demonstrates that this structure affects other economies' motivation to engage in regional financial cooperation. The second paper addresses how volatility in the Fed’s balance sheet affects the level of support for incumbent regimes in other countries. It finds that the effects differ significantly between democracies and autocracies for those with higher reliance on the global financial market. The third paper builds on theories of middle power behavior and emerging economy financial statecraft to develop a theory of middle power financial statecraft and applies it to South Korea

Characterization of degrading bioabsorbable polymeric knitted scaffolds in vitro

2025Stem cell scaffolds play a crucial role in tissue repair following damage. Effective stem cell therapy is enabled through seeding of cells on to biomimetic scaffolds, however, effective scaffolds are difficult to realize due to the complex mechanics of tissue they need to emulate. Polymeric knitted scaffolds, constructed with heat-extruded multifilament yarn, could be used to better match mechanical properties of tissue because of their anisotropic nature and ability to uncrimp similarly to human tissue. Additionally, the knitted scaffolds can be tuned to match a wide range of soft tissue mechanics while the biomaterials undergo degradation. The goal of this research is to test and characterize polymeric knitted stem cell scaffolds’ tensile properties through degradation. To achieve this goal, the polymeric knitted scaffolds were tested while simulating the environment and conditions of the human body and over a period of time long enough for degradation to occur, so that the changes in mechanical properties could be understood as the scaffolds degrade. Different polymeric yarn materials were characterized that compose the knits, revealing trends in how the mechanical properties of the materials change over time. These results will help create knitted scaffolds with adjustable degradation and tunable mechanical properties to better match the properties of injured tissue during healing

On the large-time behaviour of affine Volterra processes

We show the existence of a stationary measure for a class of multidimensional stochastic Volterra systems of affine type. These processes are in general not Markovian, a shortcoming which hinders their large-time analysis. We circumvent this issue by lifting the system to a measure-valued stochastic PDE introduced by Cuchiero and Teichmann, whence we retrieve the Markov property. Leveraging on the associated generalised Feller property, we extend the Krylov-Bogoliubov theorem to this infinite-dimensional setting and thus establish an approach to the existence of invariant measures. We present concrete examples, including the rough Heston model from Mathematical Finance.First author draf

Choral conductor perceptions of ensemble success and failure: an application of dimensional attribution theory

2025Attribution theory literature provides various ways to discuss the relationships between success or failure and the reasons perceived to be responsible for an outcome. This study is based on Weiner’s (1986) attribution theory of achievement motivation and explores the attributions of choral ensemble performances made by choral directors who work in academic settings. The aims of this research were to determine whether participants’ own successful performances are attributed differently from those they consider unsuccessful and to determine the relationships between attribution responses and personal or work-related differences. An online survey was distributed to collect choral directors’ responses. Participants were asked about their degree backgrounds, the number of years at their current institutions, how many choral ensemble classes they conducted, the size of their institutions’ choral programs, the grade levels of students with whom they worked, whether their institutions were public or private, and demographic self-identifications. The second part of the questionnaire prompted participants to recall their most and least successful choral performances in recent history, then to rate their beliefs regarding 12 statements along a 9-point scale based on McAuley et al.’s (1992) Revised Causal Dimension Scale (CDSII). The statements corresponded to four subscales: locus, internal controllability, external controllability, and stability of the cause they believed most responsible for each prompt. A sample of N = 167 choral directors completed the questionnaire. The sample included choral directors in elementary, secondary, post-secondary, and multiple settings. Reliability testing was particularly poor on one stability test item, which was removed from subsequent analyses. Testing consisted of both parametric and non-parametric tests when applicable. Results from paired Wilcoxon and t-tests both revealed that participants significantly rated attributions of successful and unsuccessful performances differently on three subscales: locus, internal control, and stability. Among the personal and vocational questions, Pearson’s correlation revealed that years at institution negatively correlated with internal control with both success (R = -.220) and failure (R = -.227). The size of choral program was also related to internal control with success according to Spearman’s rho (ρ = -.155). External control with success was also related to level of teaching via Welch’s one-way ANOVA (F = 3.678) and related to public versus private school via independent samples t-test (t = -2.513). Finally, stability with failure significantly varied according to both gender (t = 1.982) and race (t = 02.474). Linear regression was initially used to test which variables could predict each subscale score, but no model produced sufficient R2 values. After converting subscale totals to Z-statistics and reorganizing them into ordinal quartiles, ordinal logistic regression (OLR) produced three modest but statistically significant models for locus with success, external control with success, andexternal control with failure. Alternatively, generalized linear mixed-effect model (GLMM) regression indicate at least one variable as predictive of each dimension for both success and failure. Private versus public institution, level of student taught, size of choral program, and number of ensembles appeared to predict more than one subscale. Limitations and concerns with the instrument and data are subsequently addressed. Discussion includes implications for how choral ensemble leaders might more intentionally reconsider the possible reasons for their own performance disappointments and how they convey those reasons to others. Results may additionally help those who serve in mentorship or adjudication capacities. Directions for future research are also offered

Investigation of the pyruvate: ferredoxin oxidoreductase and its redox partner, ferredoxin

2024On a global scale, Fe–S clusters (among other essential redox cofactors, such as hemes, flavins, or amino acid residues) drive the core metabolic reactions of life by transporting electrons through a suite of redox-active enzymes—the oxidoreductase superfamily (Fuchs, 2011). On a per-active-site basis, these enzymes are the most efficient catalysts for several chemical reactions crucial for renewable energy (storage and usage); thus, understanding their inner workings is paramount for developing alternative, green technologies (Reda et al., 2008; Wang et al., 2014). Therefore, this dissertation examines the mechanistic principles of one class of enzyme catalyst bearing Fe–S clusters, the 2-oxoacid:ferredoxin oxidoreductase (OFOR) enzyme superfamily. Although OFORs are considered reversible enzymes, they appear to have an inherent bias toward either the reductive or oxidative chemistry, often believed to reflect the native function of the enzyme (Li et al., 2016). However, revealing the factors that influence an enzyme’s directionality has been difficult. Therefore, by examining a series of unique OFOR enzymes and mutants, this dissertation addresses the following questions: Given the diversity of OFOR enzymes (i.e., cofactor content, number of subunits, and domain modularity), what factors control catalytic bias of CO2 fixation or evolution? What role does the OFOR’s partner protein, ferredoxin, play in biasing reaction directionality? The electrochemical study of the structurally unique PFOR enzymes from C. tepidum, M. marinus, and M. acetivorans will provide critical information regarding the significance of the domain and structure composition of an OFOR enzyme. It will further reveal whether the native function of the enzyme influences the resting-state reduction potentials of the [4Fe–4S] clusters in the ET chain. Detailed site-directed mutagenesis studies of the Ct PFOR will provide a foundation for understanding the relationship between cluster potentials and ET/catalytic rates of the OFOR family and give insight into the role the protein matrix plays in tuning cluster potentials. Furthermore, electrocatalytic studies of the Ct PFOR/Fd system will provide an example of how the identity of a partner protein could direct or support enzyme catalysis and elucidate factors that contribute to successful intermolecular-ET. Understanding how Fd characteristics influence catalysis applies to many other biological systems, including the chemistry of hydrogenases or nitrogenases. Finally, the study of the Fe proteins from the nitrogenase provides insight into the thermodynamic driving force that initiates nitrogen fixation in the catalytic component of the nitrogenase, further improving our understanding of how the nitrogenase accomplishes its chemistry.2027-02-07T00:00:00