Stephan Wojtowytsch

Stephan Wojtowytsch is an Assistant Professor in the Department of Mathematics at the University of Pittsburgh, part of The Dietrich School of Arts and Sciences. His roles include being a core faculty member of the Center for Approximation and Mathematical Data Analytics and a research affiliate at Texas A&M University's Institute of Data Science. Previously, he held postdoctoral positions at Princeton University and Carnegie Mellon University. He holds a PhD from Durham University and a Master's from the University of Heidelberg.

His research focuses on the mathematical foundations of deep learning, including stochastic optimization, approximation theory, and applications in solving partial differential equations. Techniques in his work involve stochastic processes, functional analysis, and measure theory. He also explores geometric variational problems in materials science.

His recent articles address topics like momentum-based optimization, neural network approximation, and solving PDEs with machine learning methods. Supported by an NSF grant (DMS 2424801), his work bridges theoretical mathematics and computational applications.

Wojtowytsch organizes the Mathematics of Machine Learning seminar series and the CAMDA Conference. His contributions span both academic research and collaborative initiatives in data science and applied mathematics.