Boris Bonev is a Monge Assistant Professor in Applied Mathematics at École Polytechnique, where he is a member of the Centre de Mathématiques Appliquées (CMAP) and Hi! PARIS. His work focuses on Scientific Machine Learning and the Numerics of Machine Learning. More broadly, his research lies at the intersection of numerical methods and machine learning for science and engineering, with emphasis on reliable and trustworthy AI. Previously, he was a Senior Research Scientist at NVIDIA Research in Zurich.
He completed a Ph.D. in Applied Mathematics at the Swiss Federal Institute of Technology (EPFL) with Jan S. Hesthaven, after degrees in Physics and Aerospace Engineering from the University of Stuttgart. At NVIDIA, he worked under Anima Anandkumar and Alex Keller, and collaborated closely with Thorsten Kurth, Michael Pritchard, and William D. Collins. His recent work has focused on geometric neural operators for weather and climate prediction—including Spherical Fourier Neural Operators and related methods that have been adopted in FourCastNet, Huge Ensembles, ACE, and many other models. Earlier work spans hierarchical-matrix preconditioners and discontinuous Galerkin methods for wave problems.
