Bio
Hunor Csala is a Postdoctoral Research Associate in the Multiscale Materials group at Oak Ridge National Laboratory. He holds a BS and MS in mechanical engineering from the Budapest University of Technology and Economics, and earned his PhD in mechanical engineering from the University of Utah, where he conducted research at the Scientific Computing and Imaging (SCI) Institute. During his doctoral studies, he also worked as a graduate research assistant at Los Alamos National Laboratory.
Hunor’s research lies at the intersection of scientific machine learning and computational physics, with a focus on developing large-scale AI models for complex fluid dynamics problems. He has applied neural networks and classical machine learning methods to nonlinear dimensionality reduction, denoising, and reduced-order modeling, particularly in cardiovascular flow applications. He also has experience in high-fidelity computational fluid dynamics (CFD) simulations for both biomedical and industrial systems, including blood flow and laser welding. His long-term goal is to unify data-driven and first-principles models to accelerate discovery in multiscale, multiphysics systems.
Education
PhD, Mechanical Engineering, University of Utah, UT, 2025
MS, Mechanical Engineering, Budapest University of Technology and Economics, Hungary, 2021
BS, Mechanical Engineering, Budapest University of Technology and Economics, Hungary, 2019