Jonas E. Katona

Jonas Katona joined the Mathematics in Computation (MiC) section at ORNL as a Householder Fellow in June 2026.

Through prior research at NASA Langley Research Center, NASA Jet Propulsion Laboratory, Lawrence Livermore National Laboratory, and Yale University, Jonas has worked on a broad range of problems in scientific computing, including optimization algorithms for multi-player games, robust methods for invariant subspace discovery, uncertainty quantification for reduced-order models, and numerical methods for PDEs. These projects have spanned several application areas, including theoretical computer science, atmospheric science, and data-driven modeling.

Jonas’s current work focuses on developing methods to improve the efficiency, robustness, and interpretability of surrogate models while also quantifying their uncertainty and performance. He is also interested in collaborating across the lab on broader problems in scientific computing, numerical optimization, scientific machine learning, reduced-order modeling, and uncertainty quantification across various scientific and engineering applications.