Georg Stadler

About

Georg Stadler is a Professor of Mathematics at NYU's Courant Institute. His research focuses on solvers for large-scale PDE systems, uncertainty quantification, and scientific computing and machine learning. He works on Bayesian inverse problems, extreme event probability estimation, PDE-constrained optimization, and optimization under uncertainty, with applications in climate, plasma physics, and computational earth science.

Education

• Ph.D. (Dr.), Mathematics, University of Graz, Austria, 2004
• M.S. (Mag.), Mathematics, University of Graz, Austria, 2001
• M.S. (Mag.), Mathematics and Geometry Education, Graz University of Technology and University of Graz, Austria, 2001

Selected publications

• Robust multigrid techniques for augmented Lagrangian preconditioning of incompressible Stokes equations with extreme viscosity variations
• Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis
• Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet