Alexander Litvinenko

Dr. Alexander Litvinenko Copyright: © Martin Braun
Lehrstuhl für Mathematics for Uncertainty Quantification


Building: 1953

Room: 164

Pontdriesch 14-16

52062 Aachen


Phone: +49 241 80 99203


Alexander Litvinenko is Post Doctoral Research Associate and Group Leader in Uncertainty Quantification at RWTH Aachen. Previously he worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). After five years as a research scientist at KAUST (in various groups: the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification, Extreme Computing Research Center, and Bayesian Computing), Alexander moved to RWTH Aachen. He earned B.S. and M.S. degrees working on data analysis at Sobolev Institute of Mathematics at the Novosibirsk State University. During his undergraduate study, his research interests were related to the building of optimal decision trees for various areas of applications. Alexander did his Ph.D. in a group of Prof. Hackbusch at Max-Planck-Institut fuer Mathematik in Leipzig, Germany. His Ph.D. research focus was a combination of domain decomposition methods and hierarchical matrices for solving elliptic PDEs with jumping and strongly oscillatory coefficients.


Research Focus

Very often mathematical models (given by partial or ordinary differential equations) contain parameters that are uncertain. Typical examples are conductivity coefficients in groundwater flow problems and porosity. The uncertain heterogeneity in the material can affect the system behavior dramatically. One of the most used techniques to model such uncertainties is random fields. To solve the resulting PDE with random fields (stochastic PDE) numerically one has to discretize the deterministic operator as well as the high-dimensional stochastic operator. There are different methods to discretize and solve these stochastic PDEs: stochastic Galerkin, collocation, sparse grids, (quasi) MC, etc. To reduce the computational complexity the stochastic forward problem is approximated in a low-rank/sparse tensor data format. His current research interests are efficient numerical methods for solving multi-parametric/stochastic PDEs; multi-linear algebra for analyzing large data and low-rank/sparse tensor methods for uncertainty quantification; uncertainty quantification in inverse problems and data assimilation; and Spatio-temporal statistics. His research is motivated by real-world applications in the areas of subsurface hydrology, oil recovery, vadoze zone hydrology, aerospace engineering, and so on. He has made some important contributions to fast numerical techniques using low-rank tensor approximations to solve stochastic PDEs; inexpensive functional generalized polynomial approximation of classical Bayesian update formula; theory and numerical methods for approximation of large covariance matrices in spatial statistics using low-rank concepts; probabilistic/stochastic methods to model and quantify uncertainties in coefficients, parameters, and computational geometry. He has collaborated with researchers from different areas including geoscientists, engineers, and statisticians.


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