Sergey Samsonov received his bachelor's degree in 2017 from Moscow State University, Faculty of Computational Mathematics and Cybernetics, Department of Mathematical Statistics. His thesis was "Statistical Analysis of Rounded Data", advised by 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 received his master's degree in 2019, joint MSc program of Skoltech and Higher School of Economics "Statistical Learning Theory", advised by Alexey Naumov. His thesis was "Concentration Inequalities for Functionals of Markov Chains with Applications to Variance Reduction". He will earn his PhD in 2022 from the Higher School of Economics, Department of Mathematics, advised by 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.