Sergey Samsonov
- Lecturer:Faculty of Computer Science / Big Data and Information Retrieval School
- Research Assistant:Faculty of Computer Science / International Laboratory of Stochastic Algorithms and High-Dimensional Inference
- Postgraduate Student:Faculty of Mathematics
- Sergey Samsonov has been at HSE University since 2018.
Education
Bachelor's in Applied Mathematics and Information Science
Lomonosov Moscow State University
Young Faculty Support Program (Group of Young Academic Professionals)
Category "New Researchers" (2020)
Postgraduate Studies
2nd year of study
Approved topic of thesis: Concentration inequalities for functionals of Markov chains and applications to variance reduction in MCMC
Academic Supervisor: Spokoiny, Vladimir
Courses (2020/2021)
- Calculus 2 (Bachelor’s programme; Faculty of Computer Science; 2 year, 1-4 module)Rus
- Modern Methods of Data Analysis: Stochastic Calculus (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Eng
- Past Courses
Publications5
- Article Belomestny D., Iosipoi L., Moulines E., Naumov A., Samsonov S. Variance reduction for dependent sequences with applications to Stochastic Gradient MCMC // SIAM-ASA Journal on Uncertainty Quantification. 2021
- Article Belomestny D., Moulines E., Samsonov S. Variance reduction for MCMC methods via martingale representations // Working papers by Cornell University. Series math "arxiv.org". 2020
- Article Belomestny D., Moulines E., Iosipoi L., Naumov A., Samsonov S. Variance reduction for Markov chains with application to MCMC // Statistics and Computing. 2020. No. 30. P. 973-997. doi
- Article Samsonov S., Ushakov N., Ushakov V. Estimation of the Second Moment Based on Rounded Data // Journal of Mathematical Sciences. 2019. Vol. 237. No. 6. P. 819-825. doi
- Chapter Samsonov S., Ushakov N., Ushakov V. Consistent variance estimation based on rounded data, in: Book of abstracts of XXXIV International Seminar on Stability Problems for Stochastic Models. August 25–29, 2017. Debrecen, Hungary. , 2017. P. 121-121. (in press)
Conferences
- 2020
Math of Machine Learning (HDI Lab Summer School) (Sochi). Presentation: Variance reduction for MCMC methods via martingale representations
- 2019
New frontiers in high-dimensional probability and statistics 2 (Москва). Presentation: Concentration Inequalities for Functionals of Markov Chains with Applications to Variance Reduction
Structural Inference in High-Dimensional Models 2 (Пушкин). Presentation: Variance Reduction for Dependent Sequences via Empirical Variance Minimisation
SDEs/SPDEs: Theory, Numerics and their interplay with Data Science (Ираклион). Presentation: Variance reduction for dependent sequences via empirical variance minimisation
