Audience: Graduate Students

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…

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…

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…

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,…

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…

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…

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…