Maxim Kaledin
- Associate Professor:Faculty of Computer Science / Big Data and Information Retrieval School
- Maxim Kaledin has been at HSE University since 2015.
Responsibilities
Research in Deep Learning for sound applications.
Teaching on Applied Mathematics and Software Engineering programs (Faculty of Computer Science).
Courses (2023/2024)
Applied Statistical Data Analysis (Bachelor’s programme; Faculty of Computer Science; field of study "01.03.02. Прикладная математика и информатика", field of study "01.03.02. Прикладная математика и информатика"; 3 year, 1, 2 module)Rus
- Deep Learning for Sound Processing (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Rus
Probability Theory (Bachelor’s programme; Faculty of Computer Science; field of study "01.03.02. Прикладная математика и информатика", field of study "09.03.04. Программная инженерия"; 2 year, 1, 2 module)Rus
- Statistical Learning Theory (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Eng
- Statistical Learning Theory (Bachelor’s programme; Faculty of Computer Science; 3 year, 1, 2 module)Eng
- Past Courses
Courses (2022/2023)
- Applied Statistical Data Analysis (Bachelor’s programme; Faculty of Computer Science; 3 year, 1, 2 module)Rus
- Mathematical Foundations of Reinforcement learning (Master’s programme; Faculty of Computer Science; 2 year, 2 module)Eng
- Statistical Learning Theory (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Eng
- Statistical Learning Theory (Bachelor’s programme; Faculty of Computer Science; 3 year, 1, 2 module)Eng
- Statistical Learning Theory (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Eng
- Statistical Learning Theory (Mago-Lego; 1, 2 module)Eng
- Stochastic Processes (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
Courses (2021/2022)
- Mathematical Foundations of Reinforcement learning (Master’s programme; Faculty of Computer Science; 2 year, 2 module)Eng
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 3 year, 3, 4 module)Rus
- Stochastic Processes (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
Courses (2020/2021)
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 3 year, 3, 4 module)Rus
Courses (2019/2020)
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 3 year, 3, 4 module)Rus
Publications3
- Preprint Belomestny D., Kaledin M., Golubev A. Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization / -. Series - "-". 2022. doi
- Chapter Kaledin M., Moulines E., Naumov A., Tadic V., Wai H. Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise, in: Proceedings of Machine Learning Research Vol. 125: Proceedings of Thirty Third Conference on Learning Theory. , 2020. P. 2144-2203.
- Article Belomestny D., Kaledin M., Schoenmakers J. Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm // Mathematical Finance. 2020. Vol. 30. No. 4. P. 1591-1616. doi
Employment history
Research
Dec 2020 - Aug 2023 Research Fellow, HDI Lab, Higher School of Economics, main topics: stochastic optimal control, high-dimensional probability theory.
Apr 2018 - Dec 2020 Research Intern, HDI Lab, Higher School of Economics, main topics: stochastic optimal control, high-dimensional probability theory.
Jun-Aug 2018 Research Intern, Huawei Moscow Research Center, main topics: antenna modelling, FDD systems.
Teaching
Sept. 2023 - now Associate Professor, HSE Faculty of Computer Science.
Sept. 2022 - now Senior Lecturer, HSE Faculty of Computer Science.
2017-June 2022 Lecturer, HSE Faculty of Computer Science.
Sept-Dec 2019 Lecturer, Stochastic Calculus, OZON Masters School.
Sept-Dec 2018 Teaching Assistant, Stochastic Calculus, MS-1 course (HSE, Faculty of Computer Science, Statistical Learning Theory MS program).
Sept-Dec 2016 Teaching Assistant, Numerical Methods, third-year bachelor's course (HSE faculty of Computer Science).
Apr-Jun 2015,2016,2017 Teaching Assistant, Abstract Algebra, first-year bachelor's course (HSE Faculty of Computer Science).
First Cohort Graduates from Master’s Programme in Statistical Learning Theory
The Master's Programme in Statistical Learning Theory was launched in 2017. It is run jointly with the Skolkovo Institute of Science and Technology (Skoltech). The programme trains future scientists to effectively carry out fundamental research and work on new challenging problems in statistical learning theory, one of the most promising fields of science. Yury Kemaev and Maxim Kaledin, from the first cohort of programme graduates, sat down with HSE News Service to talk about their studies and plans for the future.
First Cohort Graduates from Master’s Programme in Statistical Learning Theory
The Master's Programme ‘Statistical Learning Theory’ was launched in 2017, and is run jointly with the Skolkovo Institute of Science and Technology(Skoltech).