John received his PhD in Chemical Engineering from the University of Louisville in Spring 2026. His research spanned computational fluid dynamics, machine learning, and computer vision, with applications in coronary artery hemodynamics and tear film dynamics. Outside of academia, he has served as a Machine Learning Lead, building and deploying advanced computer vision pipelines for enterprise applications, as well as an Applied Machine Learning Engineer, developing agentic solutions for enterpise with a focus on data security. John is passionate about leveraging simulation and machine learning to drive innovation in healthcare and improve patient outcomes.
Jacob received his PhD in Chemical Engineering from the University of Louisville in 2025, where his research focused on computational fluid dynamics simulations of coronary arteries and the advancement of diagnostic techniques for coronary artery disease. Following his academic work, Jacob has applied his engineering foundation in industry as a Senior Machine Learning Engineer, developing and deploying advanced ML systems on cutting-edge enterprise platforms. He is passionate about bridging rigorous computational methods with real-world applications to improve diagnostic and predictive capabilities in healthcare.