Georg Stadler

Associate Professor
Courant Institute, New York University


PhD (2004), Mathematics, University of Graz, Austria
Postdoc, Research Associate, Research Scientist and Lecturer (2006-2014), The University of Texas at Austin

DiaMonD Research

Bayesian inverse problems, Optimal design and Optimization under uncertainty, Dimension reduction, Scalable solvers for ice sheet dynamics, multigrid

DiaMonD Collaborations

Alexanderian (NC State), Biros (UT Austin), Bui-Thanh (UT Austin), Ghattas (UT Austin), Isaac (U Chicago), Petra (UC Merced), Sundar (Univ. of Utah)


Georg Stadler is an Associate Professor of Mathematics at New York University’s Courant Institute of Mathematical Sciences. His research focuses on Bayesian inverse problems, on optimal experimental design and optimization under uncertainty for systems governed by PDEs. Moreover, he works on the development of large-scale multi-level iterative solvers for systems of PDEs, discretized by higher-order methods. This works is mainly driven by non-Newtonian fluid problems arising in modelling the dynamics of ice sheets and the flow in earth’s mantle.

Impact of DiaMonD

Before joining the Courant Institute in 2014, Stadler was a Research Scientist in Ghattas’s group at UT Austin, and was partially supported by DiaMonD. His research contributions on the approximation of Bayesian inverse problems, on optimal experimental design and on optimization under uncertainty resulted from close collaboration with DiaMonD PI Ghattas, and with Alexanderian (NC State, formerly at UT Austin), Petra (UC Merced, formerly at UT Austin) and Bui-Thanh (UT Austin). His research on multi-level solvers and in particular multigrid was a close cooperation with DiaMonD PI Biros (UT Austin) and Sundar (Univ. of Utah, formerly at UT Austin). DiaMonD created a critical mass of young researchers with diverse backgrounds (Mathematics, CS, Engineering) to collaborate with each other. The driving applications helped to stay focused, and DiaMonD’s broad cross-cutting themes and regular meetings were a unique chance to establish new collaborations and independent research. Stadler is currently working on Gaussian random field priors for Bayesian inverse problems, and he is continuing active collaborations with Alexanderian and Petra, and with Ghattas and several students and postdocs of Ghattas’ team at UT Austin.