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Bio

Hunor Csala is a Postdoctoral Research Associate in the Multiscale Materials group at Oak Ridge National Laboratory. His work bridges scientific machine learning and computational physics, with a focus on developing large-scale AI models for complex fluid dynamics problems. He is broadly interested in advancing data-driven and physics-informed approaches for scientific computing. In previous research, he applied neural network architectures and classical machine learning techniques for nonlinear dimensionality reduction, denoising, and reduced-order modeling of cardiovascular flows. He has also conducted high-fidelity CFD simulations for both biomedical and industrial applications, including blood flow and laser welding. His long-term goal is to integrate data-driven and first-principles models to accelerate discovery in multiscale, multiphysics systems.

Hunor holds a BS and MS in mechanical engineering from the Budapest University of Technology and Economics. He earned his PhD in mechanical engineering at the University of Utah, conducting 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.
 

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