Georg Stadler

Georg Stadler is a Professor of Mathematics and Computer Science at New York University, affiliated with the Department of Mathematics. His research interests include parallel scientific computing, inverse problems, PDE-constrained optimization, variational inequalities, and computational earth sciences. Stadler's work focuses on solvers for large-scale PDE systems, uncertainty quantification, and scientific computing and machine learning. His research is driven by applications in climate, plasma physics, and computational earth science. He is based at Warren Weaver Hall, Office 929, and can be contacted via email at stadler@cims.nyu.edu.

Georg Stadler is a researcher at the Courant Institute, New York University, specializing in applied mathematics with a focus on inverse problems, PDE-constrained optimization, and extreme events. His work integrates scientific machine learning (sciML) to address complex challenges in geophysics and computational modeling. Stadler's research includes developing computational frameworks for Bayesian inverse problems, such as seismic inversion and ice sheet flow, as well as scalable algorithms for uncertainty quantification in large-scale PDE systems. His contributions span high-order numerical methods for wave propagation, optimal control of PDEs, and efficient solvers for geophysical and mantle convection simulations.