Maxim Kaledin
- Research Fellow: Faculty of Computer Science / AI and Digital Science Institute / Laboratory of Methods for Big Data Analysis
- 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).
Education and Degrees
HSE University
HSE University
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Courses (2024/2025)
- Applied Statistical Data Analysis (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science, field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Applied Statistical Data Analysis (Mago-Lego; 1, 2 module)Rus
- Deep Learning for Sound Processing (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Rus
- Mathematics for Data Analysis (Mago-Lego; 1-3 module)Rus
- Mathematics for Data Analysis (Master’s programme; Faculty of Computer Science; 1 year, 1-3 module)Rus
- Probability Theory (Bachelor’s programme; Faculty of Computer Science; 2 year, 1-3 module)Rus
- Past Courses
Courses (2023/2024)
- Applied Statistical Data Analysis (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science, field of study Applied Mathematics and Information Science, field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Deep Learning for Sound Processing (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Rus
- Mathematical Statistics (Bachelor’s programme; Faculty of Computer Science; 2 year, 3, 4 module)Rus
- Probability Theory (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science, field of study Software Engineering; 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
- Stochastic Calculus (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
- Symbolic Computation (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
Courses (2022/2023)
- Applied Statistical Data Analysis (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science, field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Computational Learning Theory (Mago-Lego; 1 module)Eng
- Computational Learning Theory (Master’s programme; Faculty of Computer Science field of study Applied Mathematics and Informatics, field of study Applied Mathematics and Informatics; 2 year, 1 module)Eng
- Mathematical Foundations of Reinforcement learning (Mago-Lego; 2 module)Eng
- 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
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).