Narrative sandbox games rely heavily on simulation to produce emergent narrative experiences. Instead of featuring pre-authored central narratives, these games provide a play space, a sandbox, of various systems and mechanics players use to produce interesting narrative moments. This dissertation focuses on social simulation games, a subset of narrative sandbox games that drive the narrative […]

Online communities tell stories with the games they play. As continual updates, recurring monetization, and platforms for community discussions have flourished, we have seen a rise in video games using ongoing development to tell stories, and have a community interact with those stories and build upon them. In this dissertation, I study this phenomenon, which […]

T cell receptors (TCRs) mediate antigen-specific immune responses through recognition of peptides presented by major histocompatibility complex (pMHC) molecules. Accurately predicting TCR–pMHC interactions remains a major barrier to TCR-based immunotherapy, due to limitations in current models that fail to generalize beyond common viral epitopes and well-characterized HLA alleles. In this PhD proposal, I outline a […]

The explosive growth of genomic data, driven by advances in sequencing and inference technologies, presents both an opportunity and a challenge for evolutionary biology and public health. Existing visualization and analysis tools often fall short in handling the scale, complexity, and uncertainty of modern genomic datasets—especially in the areas of pathogen surveillance and ancestral recombination […]

Neurodevelopmental and neuropsychiatric disorders (NPDs), such as autism spectrum disorder and schizophrenia, are among the most heritable yet mechanistically complex conditions. While large-scale genomic studies have identified hundreds of high-confidence risk genes, the lack of scalable and integrative platforms has limited our ability to functionally characterize these genes. To address this, I am developing a […]

Machine learning endows autonomous systems to uncover underlying structures and physical laws from measured data and to leverage these models for prediction and decision-making. As the costs of data acquisition, processing, and storage decline—and sensors become increasingly widespread alongside ever-improving algorithms—artificial intelligence has attracted significant attention in research and industry. Machine-learning methods are particularly attractive […]

Collaborative virtual reality environments have the potential to greatly impact scientific progress, especially those relating to existential human problems. Within these virtual environments, scientists could view and interact with spatial data in applications as part of their sensemaking process, however, there are design challenges and barriers to development. This advancement document presents research questions related […]

Program verification has been applied in many contexts (including videogames), but the scale and complexity of the examples that have been analyzed fall short of the ability to analyze many existing games without massive computational costs. My research focuses on automatic analysis and design of one particular game: Super Metroid, with the goal of creating […]

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence […]

Modern machine perception is powerful yet brittle: failing in response to subtle data adversaries and lacking mechanisms to learn from their errors. We address this challenge by progressing from diagnosing such failures to developing a framework for persistent learning. We first investigate the sources of this fragility, demonstrating how both naturally occurring adversarial artifacts, like […]

This thesis investigates the use of deep learning for classifying wound infections from photographic images, using colony-forming unit (CFU) counts as a quantitative labeling standard. Leveraging the visual information in wound photographs and the clinical relevance of bacterial burden, the study implements a multi-task U-Net architecture for both image reconstruction and binary classification in a […]

Accurate assessment of wound healing progress is critical for optimizing patient care and preventing complications, yet clinicians currently lack precise tools to determine where a wound stands in the healing timeline. Wound healing progresses through overlapping stages of inflammation, proliferation, and maturation, each marked by characteristic shifts in gene expression that are difficult to interpret […]

The rapid integration of artificial intelligence and machine learning into clinical practice has driven advances in disease classification, segmentation, and clinical decision support. However, the complexities of medical data pose a challenge to widespread adoption. The rarity of medical conditions, ethical considerations, and varying acquisition protocols leads to limited and noisy data. The time-intensive process […]

Digital technologies have made large-scale online interaction a central part of how people communicate, connect, and work together. Yet scaling often comes at the cost of depth, and interactions can become superficial and chaotic, drifting away from the richer interactional contexts of small-scale or in-person settings that support trust and meaningful exchange, and that make […]

Live-cell fluorescence microscopy enables high-resolution, non-invasive imaging and optical stimulation of biological processes, yet existing systems are often prohibitively expensive, mechanically complex, and poorly suited for wide field-of-view, high-speed, multi-channel experiments—particularly those involving optogenetics. To address these limitations, I developed a low-cost, modular fluorescence microscope constructed from 3D-printed mechanical components and off-the-shelf optics. I also […]

Open-domain question answering (ODQA) systems come with diverse challenges — ranging from resolving conflicting information to interpreting figurative expressions and representing meaning in a human-understandable form. This dissertation presents three complementary contributions toward building more robust and interpretable QA systems. First, we investigate QA model performance on figurative language. Introducing FigurativeQA, a benchmark of yes/no […]

Transfer RNA (tRNA) modifications are essential for structural integrity, decoding fidelity, and stress adaptation, yet their dynamics across phylogenetically distinct archaeal species and their functional roles during stress remain incompletely understood. This dissertation aims to address some of these gaps through a multi-scale investigation that spans the evolutionary dynamics, stress-responsive functions, and direct detection of […]

Local ancestry methods classify the segments of DNA inherited from a specific ancestry (e.g., African, East Asian, European), improving analyses of admixed populations. Aim 1 applies ancestry-specific analysis to more than 1000 whole genome sequences across Polynesia, revealing strong bottlenecks in the voyagers who settled both Hawaiʻi and Rapa Nui, and confirming frequencies of Mendelian […]