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Leader of research group on tensor numerical methods (till 2016)

DrSci. Boris N. Khoromskij

Max-Planck-Institute for Mathematics in the Sciences

Inselstr., 22-26, D-04103

Leipzig, Germany

e-mail: bokh{at}mis.mpg.de

In 1968 Boris Khoromskij has finished a special mathematical school No. 18 in Moscow, where the director was the world-known Mathematician, member of the Russian Academy of Science, Professor Andrey N. Kolmogorov. In 1973 Boris Khoromskij graduated the Mathematics Department of the Moscow State University. Then he worked as a leading scientist at the Mathematics Department of the Joint Institute for Nuclear Research (JINR) in Dubna, Moscow region, where he received his PhD in Mathematics in 1978 and the Doctor of Science degree (habilitation) in 1992. At that time Boris Khoromskij was the Head of the Numerical Methods Group at JINR.

Since 1993 Boris Khoromskij has been working in Germany at the WIAS institute in Berlin, at Heidelberg University, Stuttgart University and the University of Kiel. Since 1999 till now Boris Khoromskij is working as a senior scientist at the Max-Planck Institute for Mathematics in Leipzig, Germany (now emeritus). His field of research is mainly concerned to numerical methods for PDEs with the focus on modern tensor numerical methods for multi-dimensional problems in a wide range of applications in scientific computing. Boris Khoromskij authored 3 books and over 130 scientific articles. He advised 8 PhD students.

According to Research.com in 2023 Boris Khoromskij has

B. N. Khoromskij and G. Wittum.

Research monograph, LNCSE, No. 36, Springer-Verlag 2004.

Boris N. Khoromskij

Research monograph, De Gruyter, Berlin, 2018.

Radon Series on Computational and Applied Mathematics 19.

Venera Khoromskaia and Boris N. Khoromskij

Research monograph, De Gruyter, Berlin, 2018.

Current research topics :

Numerical analysis in higher dimensions.

Quantized-TT formats in application to quantum molecular dynamics.

DMRG and QTT methods for multidimensional boundary value, spectral and time-dependent problems.

Tensor iterative methods for stochastic and parametric PDEs.

Tensor-structured methods in electronic structure calculations.

Range-separated (RS) tensor format for summation of long-range electrostatic potentials.

Rank-structured tensor methods for calculation of the Bethe-Salpeter excitation energies.

Tensor iterative methods and low-rank spectrally close preconditioners for multidimensional

elliptic problems with application to PDE-constrained optimal control problems

and for the Poisson-Boltzmann equation in protein modeling.

Machine learning for tensor approximation in many dimensions.

last modified: September 12, 2023

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