Alexey Boyko

Education: DGAP MIPT (B.Sc, M.Sc), CSE Skoltech (M.Sc)

Research interests: Tensor Decompositions, Reinforcement Learning and Control, Computational Photonics

Thesis title: Tensor Methods in Model-Based Reinforcement Learning

Academic mobility: Rome-Moscow school of Matrix Methods and Applied Linear Algebra, University of Rome “Tor Vergata”, Department of Mathematics, September 4-18, 2016

Publications:

Boyko, A. I., Oseledets, I. V., Gippius, N. A. (2017, May). Towards solving Lippmann-Schwinger integral equation in 2D with polylogarithmic complexity with quantized tensor train decomposition. In 2017 Progress In Electromagnetics Research Symposium-Spring (PIERS) (pp. 2329-2333). IEEE.

Conferences:

Boyko A.I., Oseledets I.V., Globally Optimal Continuous Control with a Sparse Reward using Tensor Train, International Conference on Matrix Methods in Mathematics and applications (MMMA-2019)