Jonathan W. Siegel

Jonathan W. Siegel is an assistant professor in the mathematics department at Texas A&M University. Prior to joining Texas A&M, he was a postdoctoral researcher at Pennsylvania State University, working with Jinchao Xu. He earned his PhD from UCLA where he worked under the direction of Russel Caflisch. Siegel’s research is concerned with approximation theory, the mathematical theory of neural networks, statistics, and numerical methods for solving partial differential equations. A particular emphasis of his work is on the theoretical development and mathematical analysis of machine-learning methods for scientific computing.