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.

HomeTeachingResearch and PublicationsEmployment historyIn HSE news

Responsibilities

Research in Deep Learning for sound applications.

Teaching on Applied Mathematics and Software Engineering programs (Faculty of Computer Science).

Education and Degrees

2023

Candidate of Sciences* (PhD)
HSE University

2019

Master's
HSE University

2017

Bachelor's
HSE University

* Candidate of Sciences
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.

Professional Interests

numerical methods Stochastic Optimal Control

Courses (2024/2025)

Applied Statistical Data Analysis (Bachelor’s programme; Faculty of Computer 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
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; 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

Timetable for today

Full timetable

Dissertation for a Candidate of Sciences degree

2023
M. Kaledin Разработка и анализ алгоритмов для задачи оптимального управления и обучения с подкреплением

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).

Aug

2019

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.

Jul

2019

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).