Sergey Samsonov recieved his Bachelor's in 2017 from Moscow State University, Faculty of Computational Mathematics and Cybernetics, department of Mathematical statistics. His thesis was "Statistical analysis of rounded data" (advisor: V. Ushakov). He has worked in probability and random processes, focused on analytical apparatus of probability theory (characteristic functions), moment inequalities for characteristic functions and deconvolution problems.
Samsonov recieved his Master's in 2019, joint MSc program of Skoltech and Higher School of Economics "Statistical Learning Theory" (advisor: Alexey Naumov). His thesis was "Concentration inequalities for functionals of Markov Chains with applications to variance reduction".
He will obtain his PhD in 2022 from Higher School of Economics, department of Mathematics. (advisor: Alexey Naumov). His current research is in Markov Chains, concentration inequalities for Markov Chains and their applications for studying theoretical properties of MCMC algorithms. He is also involved in projects on diffusion-based control variates for MCMC, project on variance reduction for Markov Chains via martingale representations and variance reduction in Reinforcement Learning.
- Theory of Reinforcement Learning, Fall 2020. Visiting Graduate Student.