My research applies advanced fluid dynamics and computational modeling to critical challenges across three interconnected domains: biomedical engineering, process safety, and applied aerodynamics. In the medical realm, I investigate pathophysiology and therapeutic delivery, modeling cerebrovascular hemodynamics for aneurysm treatment, optimizing drug aerosol transport in treating respiratory diseases, and optimizing ocular drug dosing administration. In industrial safety, I focus on hazard mitigation through risk assessment of fire-derived heat/toxicants and high-fidelity modeling of complex fires (LNG, battery runaway) and filtration systems. In aerodynamics, I optimize performance via experimental and computational analysis of airfoils and automotive designs. Methodologically, I employ a synergistic, data-integrated approach. This combines high-fidelity Computational Fluid Dynamics (CFD)-including multiphase flow, fluid-structure interaction, and non-Newtonian modeling-with Experimental Fluid Dynamics (e.g., Particle Image Velocimetry) for validation. To enhance predictive capability and efficiency, I leverage Data Science, statistical analysis, and develop Machine Learning models, including Physics-Informed Neural Networks (PINNs) as reduced-order surrogates. This integrated methodology enables a translational research pipeline from fundamental mechanistic insight to practical engineering and clinical solutions.