Engineering 2

This report presents a one-dimensional Random Choice-based hybrid method for simulating special relativistic hydrodynamics (SRHD) flow problems. The proposed scheme combines a high-order accurate method and a random choice method, selectively applying the first to smooth flows and the second to shocks and discontinuities. This hybrid approach addresses the issue of incorrect wave placements in […]

Modern applications are increasingly designed with a strong emphasis on scalability and performance, as systems are expected to process ever-growing volumes of data and deliver results with minimal latency. Techniques such as distributed architectures, in-memory computation, and optimized data structures are routinely adopted to meet these performance-driven demands. However, in the pursuit of speed and […]

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 […]

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 […]

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, […]

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 […]

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 […]