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The Hypermobility Turn: Opera of The Future, The Future of Opera
No full textAs a global artform, opera has always been on the move. Yet contemporary opera today has expanded our understanding of global flows in unprecedented ways. The COVID-19 pandemic serves as a powerful testament to how immobility can galvanize unexpected mobilities, as the creative innovations of 2020-2021 continue to generate seismic changes in our operatic ecology today. This unpredictable shift reveals a crucial insight about twenty-first century global flows: mobility is neither preordained nor stable but exists in dynamic tension with immobility, producing evolving and often unforeseen artistic possibilities. This dissertation examines the alternative mobilities shaping contemporary operatic sensibility. I investigate what it means for opera to be performed in a particular city, locale, or even cyberspace. Furthermore, in light of the pervasive racial reckoning unfolding in the performing arts, I interrogate how opera is moving beyond Western epistemes and what this shift signifies for the future of the art form.
Taking an interdisciplinary approach, I analyze cross-cultural operas that illustrate how the pandemic and decolonial thought have ushered in a watershed moment for the genre. What we could call a hypermobility turn has emerged—operas predicated on breaking barriers and hierarchies, but far beyond more conventional strategies of unsettling, beyond swapping out musical or vocal styles, adapting contemporary news as plot material, foregrounding social issues, or choosing an unexpected performance venue. The change is more deep seated. Hypermobility, as I define it, resists the frameworks of conventional mobility studies by decentering geographical specificity. Rather than tracing movement from one fixed location to another, hypermobility captures the spontaneous diffusion of innovations in opera today, demanding new modes of engagement. Through transgressive movements, media, and mechanisms, contemporary opera opens up a spectrum of artistic and political possibilities.
Chapter 1 examines Bright Sheng’s Dream of the Red Chamber, which exemplifies Sheng’s musico-diasporic aesthetic through its interweaving of Chinese music, poetry, and stylistic allusions to Béla Bartók, a synthesis that propelled its transnational success across the US, China, and Hong Kong. Chapter 2 focuses on The Industry’s Sweet Land, a site-specific, collaboratively produced opera staged at Los Angeles at the State Historic Park. I argue that the alternative site generates ephemeral and unexpected experiential orders emerging in the here-and-now, which forge a new political sensibility in opera. Chapter 3 analyzes Yuval Sharon’s Twilight: Gods, two site-specific reimaginations of Richard Wagner’s Götterdämmerung staged in urban multi-storied garages in Detroit and Chicago. I argue that this production serves as a critical site for investigating how alternative stagings reveal underlying ideologies of canonic operas while raising complex questions about localization and reinterpretation. Finally, Chapter 4 focuses on White Snake Projects’ The Pandemic Trilogy, a set of three live virtual operas produced during COVID-19. While proponents of cyber-stage view transmedia storytelling as a promising avenue of operatic engagement, I posit that the trilogy raised new questions about the challenges transmedia aesthetics have introduced into opera performance, asking us to put pressure on the idea that live virtual opera heralds a paradigm shift for the future of opera.Musi
Simulation and Analysis of Electrochemical Systems
Full text linkPorous electrodes are essential components in redox flow batteries, a promising technology for long duration, grid-scale energy storage, which will be a vital part of the clean energy transition. Carbon capture and storage (CCS) can mitigate and eventually reverse global warming. In this thesis I present four works, in which a porous electrode and a CCS system are each subject to one simulation and one analysis. First, I show a 3D digital twin for porous electrodes that uses direct numerical solution of the governing Navier-Stokes and Nernst-Plank equations for incompressible flow and electrochemical mass transport with Butler-Volmer reaction kinetics. Our performant, open source code handles systems approaching a billion cells at 1.25 μm resolution on a single workstation, and will scale well on modern scientific supercomputers. This work also includes a novel reformulation of the steady state concentration problem, and introduces a figure of merit, the mass-transport limiting utilization of an electrode Umt. Second, I simulate the steady state concentrations in an electrochemical acid-base generator that was experimentally characterized by my collaborators and is suitable for CCS. Third, I solve for the equilibrium concentrations in another experimental CCS system, in which aqueous quinones capture CO2 via both pH-swing and nucleophilicity swing mechanisms. Finally, I perform an elaborate nonlinear iterative calibration to measure the state of charge of an operating porous electrode given experimental image intensity and electrochemical data obtained by fluorescence microscopy.Engineering and Applied Sciences - Applied Mat
Topics in Privacy, Data Privacy and Differential Privacy
Full text linkIn an era of unprecedented data availability and analytic capacity, the protection of individuals' privacy in statistical data releases is becoming an increasingly difficult problem. This dissertation contributes to the theoretical and methodological foundations of statistical data privacy, largely focusing on differential privacy (DP). We begin with a multifaceted investigation into privacy from legal, economic, social, and philosophical standpoints, before turning to a formal system of DP specifications built around five core building blocks found throughout the literature: the domain, multiverse, input premetric, output premetric, and protection loss budget. This system is applied to statistical disclosure control (SDC) mechanisms used in the US Decennial Census, analyzing both the traditional method of data swapping and the contemporary TopDown Algorithm. Beyond these case studies, this dissertation explores the inferential limitations posed by DP and Pufferfish privacy in both frequentist and Bayesian settings, establishing general bounds under mild assumptions. It further addresses the challenges of applying DP to complex survey pipelines, incorporating issues such as sampling, weighting, and imputation. Finally, it contextualizes DP within broader frameworks of data privacy, namely the Five Safes and contextual integrity, advocating for a more integrated approach to privacy that respects statistical utility, transparency, and societal norms.Statistic
Debugging and Help-seeking with Chatbots in CS1
Full text linkFor many beginner programmers, encountering errors in code can be frustrating and disheartening—leading some to questions their belonging in computer science (CS). In these moments, timely debugging help is essential to sustain motivation and foster learning. While students have traditionally turned to peers or teaching assistants for guidance, many now seek debugging support from conversational Large Language Models (LLMs). These chatbots offer promise in providing immediate help, but their ability to generate full-code solutions raises concerns about learning and over-reliance. As these tools become more prevalent, it is important to understand how they can be used to support student's in their debugging and how students seek-help with chatbots.
This dissertation explores how students interact with chatbots in introductory computer science courses (CS1) and opportunities to support debugging. The research is presented in a three-paper format. The first paper examines past debugging interventions before the rise of LLMs, identifying gaps that these tools could potentially address. The second paper presents findings from student interviews about their experiences using a course-integrated chatbot, highlighting how they engage with the debugging assistance throughout the semester and their evolving beliefs about appropriate chatbot use. The third study analyzes naturalistic chat data and survey responses in another CS1 course to investigate how students' goal-orientation and beliefs associate with their help-seeking behaviors. The findings from this dissertation offer insights into designing course chatbots and instructional framing around chatbot use to support students' debugging and learning.Educatio
Essays on Data Science: Computational Measurement for Learning and Teaching
Full text linkTo study teaching and learning at a large scale, we must introduce new methods for the analysis of rich, unstructured data -- such as audio, video, and transcribed text -- from classrooms. In this dissertation, I develop and apply computational and statistical methods to measure teaching and learning processes captured in two unstructured sources: student writing and transcripts of teacher speech from classroom lessons.
My first essay introduces Coupled Likelihood Estimation (CLE), a method that improves the precision of parameter estimates in models of unstructured data features while requiring fewer expert-labeled observations. It combines information from limited samples of expert-labeled data with larger samples of data with machine-predicted labels. CLE leverages the geometric structure of the joint likelihood from both identifying (labeled) and non-identifying (unlabeled) data, constraining parameter estimates to, approximately, a surface defined by the unlabeled data's likelihood. Simulations demonstrate that CLE is unbiased, reduces root mean squared error, and yields narrower confidence intervals compared to existing methods, in some cases effectively achieving average efficiency gains equivalent to doubling the expert-labeled sample size. An application estimating the effect of an educational intervention on student writing quality illustrates CLE’s practical utility, producing estimates closer to an oracle benchmark using only 18\% of the expert-labeled data. By amplifying the value of limited labeled data, CLE lowers barriers to high-quality inference in resource-constrained domains such as healthcare, education, and policy evaluation. The method’s broad applicability, theoretical guarantees, and computational approach offer a pathway to cost-effective, reliable analyses in settings where researchers face high labeling costs.
My second essay leverages natural language processing techniques to study the use of mathematical vocabulary in elementary math classrooms. My collaborators and I develop a rules-based computational measure of mathematical vocabulary use. We find that teachers differ substantially in the amount of mathematical vocabulary they model for their students. Students of teachers in the 75th percentile were exposed to 28 more mathematical terms per lesson (4,480 per year) than students of a teacher in the 25th percentile. Observed characteristics explain very little of this variation in teachers' mathematical vocabulary use. Finally, students randomly assigned to teachers’ who used more mathematical vocabulary in previous years scored higher on standardized tests of mathematics. This implies that teachers who expose their students to more mathematical vocabulary are more effective teachers of mathematics. Across value-added studies, a teacher one standard deviation above the mean in effectiveness raises math scores by between .10 and .15 \citep{BacherHicks2023}; our estimate of the effect of being assigned to a teacher who uses one standard deviation more mathematical language accounts for roughly half of this variation, indicating that our measure is a powerful predictor of teacher effectiveness.
In my third essay, I develop Contextual Value Separation (CVS), a general method for identifying words used differently between pre-specified subsets of documents in large text corpora. CVS achieves this by combining contextual embeddings with machine learning classifiers, permutation testing, and statistical adjustments for multiple comparisons. Whereas current methods identify words that predict membership within a given class of documents, CVS reveals cases where separate classes of the documents use the same word in differing ways. For example, experienced and novice math teachers may use a mathematical vocabulary term with similar frequency but in markedly different ways or contexts. This approach can search over a specified set of target words or over the entire vocabulary of the corpus. For each target word, CVS infers how consistently its contextual embeddings differ by subset. Because vocabularies are large, the method includes multiple testing correction to control the false-discovery rate, typically yielding a small set of words whose usage varies between the document classes. After identifying the words whose usage most consistently differs, example usages from each subset are extracted for qualitative examination. CVS easily extends to other forms of unstructured data represented by embeddings, such as video and audio. The method can be used as an exploratory tool for hypothesis generation, to test a priori hypotheses, or to detect treatment effects on textual outcomes in experimental settings. To demonstrate the method, I analyze a set of transcripts from upper elementary mathematics lessons and identify two ways that teachers with larger impacts on math scores use mathematical vocabulary differently: more use of the mathematical meanings of polysemous terms and more requests that students engage with questions related to the terms. The method can be easily extended to other forms of unstructured data can be encoded into vectors, e.g., audio and video.
As a collection, these three essays reveal the promise of computational methods for enabling the analysis of text data (and other rich, unstructured data sources). They contribute several novel findings in the field of education regarding mathematical vocabulary and effective teaching. From a statistical point of view, CLE introduces a new way to leverage large amounts of machine labeled data, which, in addition to its value for educational research, can lower the cost of research in several domains, such as phenotyping electronic health records.Educatio
Democratic transitions and party institutionalization
Full text linkThis dissertation argues that variation in the party and party system institutionalization of Third Wave regimes is attributable to the mode of transition.
The first paper distinguishes between modes of transition according to the relative strength of incumbent and opposition party organizations. It categorizes 55 Third Wave transitions (1974-2001) into impositions, pacts, and collapses, showing that modes of transition are associated with distinct rules and party-voter linkage structures. Imposed transitions led by strong authoritarian incumbents put oppositions under high levels of adversity, incentivizing investment in strong party organizations and producing stable, institutionalized party systems. Conversely, pacted transitions where incumbents and oppositions agree to share power guarantee entrants easy access to votes and finance, disincentivizing investment in and adaptation of party organizations.
The second paper argues that political pacts are deleterious to party-building by incentivizing parties to collude, rely on inherited resources, and dilute their party brands. Brand dilution has negative consequences for parties’ internal cohesion, the level of mass partisanship, and parties’ public legitimacy. Evidence is marshalled from Latinobarómetro data and case studies of six parties in Mexico and Chile.
The third paper argues that political pacts are deleterious to party-building by disincentivizing investment in complex, autonomous intraparty organizations. When parties depend on state finance, hand their leaders outsized authority, and demobilize mass actors, they are unlikely to cultivate mass memberships, adhere to intraparty institutions and norms, or descriptively represent salient social groups. Evidence is marshalled from party-financial disclosures, candidates’ lists, and Asia Barometer data from South Korea and Taiwan.
The fourth paper investigates the relationship between modes of transition, party organizations, and party brands across a wide range of Third Wave democracies. Using data from 163 parties in 40 countries, I find the mode of transition durably conditions the organization- building and party-branding strategies of both authoritarian successor and opposition parties.Governmen
Underutilization of University Intellectual Property and Entrepreneurship Programs in OECD Countries: A Policy Analysis for the European Commission
No full textThe European Union stands at a pivotal moment in its economic trajectory, engaged in a fierce global competition to transition from a resource-intensive industrial model to a dynamic, knowledge-based economy. Central to this transition is the role of the university not merely as a repository of knowledge, but as an active engine of economic creation. While Europe boasts a dense network of prestigious research universities and maintains robust levels of public investment in research and development (R&D), a persistent and systemic gap remains between scientific output and commercial application. This phenomenon, widely characterized in policy literature as the "European Paradox," represents a critical underutilization of university intellectual property (IP) and entrepreneurship programs.
This report, prepared for the European Commission’s Directorate-General for Research and Innovation, provides an exhaustive policy analysis of this challenge. The client for this analysis is the European Commission, specifically the leadership responsible for the Horizon Europe framework and national innovation system coordination. The analysis proceeds from a clear problem definition: despite world-class research inputs, European universities significantly underperform in generating high-growth spin-outs, licensing revenue, and commercial patents compared to international peers, particularly the United States.
Drawing on comprehensive comparative data from the United Kingdom, the United States, Japan, and member states of the European Union, this analysis identifies four intertwined root causes of this underutilization: (1) organizational inertia and bureaucratic bottlenecks within Technology Transfer Offices (TTOs); (2) misaligned incentive structures that penalize faculty entrepreneurship and dilute founder equity; (3) deep-seated cultural resistance to commercialization within the academic guild; and (4) the fragmentation of regional innovation ecosystems which prevents the realization of critical mass (Fukugawa, 2025).
The evidence presented is compelling and urgent. In the United Kingdom—a relatively mature market within the European context—university licensing revenue and equity sales represented a mere 2.1% of total research expenditure in 2021-22, according to the UK Department for Science, Innovation and Technology (2023). Furthermore, European universities have historically demanded excessive equity stakes in spin-outs, averaging 15-30% (and often higher), compared to a norm of 2-7% in the United States (Air Street Capital, 2021; Royal Academy of Engineering, 2025). This predatory equity stance creates a "cap table" structure that is often uninvestable for venture capital, strangling promising ventures in the cradle.
This report constructs and evaluates five distinct policy alternatives to address these failures:
1. Status Quo (Baseline): Continuing current fragmented national approaches.
2. Reform and Professionalization of TTOs: A supply-side intervention focusing on capacity building.
3. Incentive Alignment: A demand-side intervention restructuring rewards for faculty and founders.
4. Collaborative Platforms: A structural intervention building regional networks.
5. Enhanced Experiential Entrepreneurship Education: A long-term human capital intervention.
Through a rigorous projection of outcomes based on the criteria of effectiveness, efficiency, equity, feasibility, and sustainability, this analysis recommends a comprehensive Systemic Level Reform. This strategy does not rely on a single lever but integrates the professionalization of TTOs, the realignment of faculty incentives through mandated "founder-friendly" policies, and the creation of collaborative regional innovation networks to overcome fragmentation. The projected outcome of this reform is a measurable increase in the velocity and volume of commercialization, transforming Europe’s research excellence into tangible economic sovereignty.Author's Origina
Towards useful computation with neutral-atom quantum processors
No full textQuantum computers promise to harness the power of quantum entanglement for transformative advances in cryptography, the computational science of materials, and our understanding of fundamental physics. However, major advances in quantum algorithms, hardware, and error-correction techniques are necessary to realize this technology's full potential.
In this thesis we show that co-design---the development of all three components in a mutually informed fashion---can result in significant advantages and enable complex quantum computation. We focus on the reconfigurable-atom-array platform, where qubits are encoded in long-lived atomic states and controlled by laser fields. Quantum entanglement at scale is realized by globally exciting the atoms to strongly-interacting Rydberg states, and we experimentally demonstrate quantum gates with fidelity above 99.5%---high-enough to benefit from error-correction (QEC). Leveraging dynamical qubit connectivity enabled by coherent atom transport, we design hardware-efficient protocols for simulating chemistry models and topological quantum matter. With this approach, we experimentally realize the exotic non-Abelian phase of the Kitaev honeycomb model and probe the emergent dynamics of fermionic particles, engineered to experience strong interactions. Similarly, we also show that the overhead introduced by QEC for classically complex computation can be significantly reduced by designing the quantum circuit and QEC codes together. These advances highlight the utility of the co-design principle and pave the way towards useful quantum computation.Physic
Integrative analysis of transcriptomics, epigenetics, and copy number to assess lineage and dynamics of tumor clones during cancer progression
No full textReconstructing dynamic biological processes, such as cancer evolution, is challenged by sparse clinical sampling and incomplete multi-omic measurements. This dissertation develops and applies integrative computational strategies to recover interpretable cell-state dynamics under these constraints, focusing on multiple myeloma (MM), chronic lymphocytic leukemia (CLL) progression to Richter's syndrome (RS), and anti-BCMA CAR-T cell therapy.
First, to model cellular dynamics from static snapshots, I contributed to scDiffEq, a neural stochastic differential equation framework that infers cellular drift and diffusion from scRNA-seq. This approach improves trajectory inference and fate prediction. My contributions included designing benchmark criteria and developing novel simulation strategies using binned CytoTRACE pseudotime, which validated the method's robustness to sparse sampling density.
Second, to reconstruct evolutionary time using genetic lineage, I developed Numbat-Multiome. This method unifies copy number variation (CNV) inference from both scRNA-seq and scATAC-seq data. By integrating coverage and allelic imbalance signals within a shared genomic binning scheme, the method accurately detects diverse events (F1 > 0.9), including copy-neutral loss-of-heterozygosity. This enables the reconstruction of subclonal phylogenies to serve as a lineage anchor for multi-omic regulatory analysis.
Applying these frameworks, I dissected regulatory heterogeneity in multiple myeloma. By profiling chromatin accessibility (scATAC-seq) across 36 patient samples, I identified differentially accessible regions and transcription factor programs associated with disease progression. In a separate study on anti-BCMA CAR-T therapy, single-cell multiome profiling of post-infusion cells characterized the coupled transcriptional and epigenetic states underlying T-cell exhaustion and linked CAR promoter accessibility to CAR expression.
To trace clonal evolution during the transformation of CLL to RS, I integrated scRNA-seq, mitochondrial scATAC-seq, and scDNA-seq from pilot cases to map clone-specific regulatory rewiring. Furthermore, I analyzed STAG-seq data from six CLL samples, which jointly profiles targeted genotype and transcriptome in the same cells. This analysis enabled the direct and unambiguous assignment of transcriptional programs and immune cell states to specific genetic subclones.
Together, these contributions provide a lineage-anchored, time-aware framework for studying tumor evolution and therapy response. By coupling mutation-defined ancestry with multi-omic regulatory readouts and neural dynamical models, this work delivers practical tools and conceptual clarity for inferring cancer cell-state transitions from limited and static clinical data.Biomedical Informatic
An integrative approach to velvet worm biodiversity and systematics
Full text linkUnderstanding patterns of biodiversity in many invertebrate groups is challenging not only due to a lack of taxonomic resolution, but also due to a lack of molecular resources to effectively delimit species and robustly assess their relationships to one another. Here, I focus on the velvet worms (phylum Onychophora), an invertebrate group with longstanding and ongoing taxonomic challenges. Systematic work in this dispersal-limited terrestrial phylum has been difficult due to their limited morphological variation and unusual aspects of their DNA, such as their large genome size, GC content, and genetic variability. However, despite these challenges evidence has grown for a high number of cryptic species within the group. In 2023 the first high quality velvet worm genome was published, followed in 2024 by an ultra-conserved element (UCE) probe set designed to help resolve phylogenetic relationships in the phylum. These advances have now made it possible to conduct comprehensive explorations of species diversity and biogeography in this little-studied phylum.
In this dissertation I focus on two genera with contrasting reproductive strategies from Australia and Aotearoa New Zealand, the oviparous Ooperipatellus, and the ovoviviparous Peripatoides. Using these genera as case studies I take an integrative and multileveled approach to the understanding of onychophoran biodiversity, combining DNA barcoding, targeted sequence capture, morphology, and ecological niche modeling to explore species diversity and distributions. In Chapter 1, I review historic taxonomic work and use DNA barcoding and single locus species delimitation approaches to generate updated estimates of species diversity for Ooperipatellus and Peripatoides in Aotearoa. In Chapter 2, I show for the first time the utility of UCEs and SNP-based species delimitation methods for determining species diversity in velvet worms, resulting in the description of three new species of Ooperipatellus from Tasmania, Australia. In Chapter 3, I use ecological niche modeling to explore how climate refugia and environmental change since the last glacial maximum have shaped the distributions of Ooperipatellus in both Australia and Aotearoa. I also identify six new putative species of Ooperipatellus in Aotearoa. Lastly, in Chapter 4, I use UCE data to explore the validity of described species in the genus Peripatoides, several of which are cryptic species with overlapping distributions. The results of my analyses reveal evidence of admixture and highlight how samples with mixed ancestry can complicate species delimitation attempts, especially when using approaches that assume discrete genetic structure.Biology, Organismic and Evolutionar