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 presence of coronary artery calcification is a strong predictor for future cardiovascular events where cardiac risk categories are quantified depending on calcification size. Dual-energy chest X-rays provide high contrast visualization to improve opportunistic screening for quantifying coronary artery calcifications, determining bone mineral density (i.e., osteoporosis) and characterizing lung lesions. As a dual-energy imaging modality, […]

WiFi-based indoor positioning is a widely researched area focused on determining the location of devices. Accurate indoor positioning has numerous applications, including asset tracking and indoor navigation. Despite advances, their adoption in practice remains limited due to several challenges such as environmental changes that cause signal fading, multipath effects, and interference, all of which reduce […]