Academic Career and Research Areas
Professor Ullmann (b. 1979) conducts research on mathematical methods of uncertainty quantification using tools from numerical analysis, data science and computational science and engineering. The goal of her work is the design and analysis of efficient algorithms and estimators for partial differential equations with random coefficients. The current focus is on Bayesian inverse problems, rare events, and optimal control with PDE constraints.
Professor Ullmann studied Applied Mathematics at the TU Bergakademie Freiberg. She obtained her doctorate in Freiberg and subsequently worked as a postdoctoral researcher in the DFG priority program "Extraction of quantifiable information from complex systems". In 2009, she was a visiting research associate at the University of Maryland, College Park. From 2011 to 2014 she was a research associate at the University of Bath in England and subsequently at the University of Hamburg. In 2015, she joined TUM as an assistant professor.
Key Publications (all publications)
Latz J, Papaioannou I, Ullmann E: “Multilevel Sequential^2 Monte Carlo for Bayesian Inverse Problems”. Journal of Computational Physics. 2018; 368: 154-178.Abstract
Ahmad Ali A, Ullmann E, Hinze M: “Multilevel Monte Carlo analysis for optimal control of elliptic PDEs with random coefficients”. SIAM/ASA Journal on Uncertainty Quantification. 2017; 5(1): 466-492.Abstract
Ullmann E, Papaioannou I: „Multilevel estimation of rare events“. SIAM/ASA Journal on Uncertainty Quantification. 2015; 3(1): 922-953.Abstract
Ullmann E: „A Kronecker product preconditioner for stochastic Galerkin finite element discretizations“. SIAM Journal on Scientific Computing. 2010; 32(2): 923-946.Abstract
Ernst OG, Powell CE, Silvester DJ, Ullmann E: “Efficient solvers for a linear stochastic Galerkin mixed formulation of diffusion problems with random data”. SIAM Journal on Scientific Computing. 2008/09; 31(2): 1424-1447.Abstract