Maxim Rakhuba
- Laboratory Head: Faculty of Computer Science / AI and Digital Science Institute / Laboratory for Matrix and Tensor Methods in Machine Learning
- Deputy Department Head, Associate Professor: Faculty of Computer Science / Big Data and Information Retrieval School / Joint Department with the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS)
- Programme Scientific Supervisor: Applied Mathematics and Information Science
- Maxim Rakhuba has been at HSE University since 2020.
- Language Proficiency
- Russian
- English
- Contacts
- Phone:
27326 - Address: 11 Pokrovsky Bulvar, Pokrovka Complex, room T918
- SPIN-RSCI: 8816-5681
- ORCID: 0000-0001-7606-7322
- ResearcherID: Q-6210-2016
- Scopus AuthorID: 55631908800
- Google Scholar
- Supervisors
- N. Vereshchagin
- E. Sokolov
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Education and Degrees
Moscow Institute of Physics and Technology
Moscow Institute of Physics and Technology
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.
Continuing education / Professional retraining / Internships / Study abroad experience
Postdoc at ETH Zurich, 2018-2020
Awards and Accomplishments
- Best Teacher — 2021–2024
- Young Faculty Support Programme (Group of Young Academic Professionals)
Category "New Lecturers" (2021–2022)
Courses (2025/2026)
- Foundations of Tensor Computations (Bachelor field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Bachelor field of study Applied Mathematics and Information Science; 4 year, 1, 2 module)Rus
- Fundamentals of Matrix Computations (Bachelor field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Master field of study Applied Mathematics and Informatics; 1 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Mago-Lego)Rus
- Past Courses
Courses (2024/2025)
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Master’s programme; Faculty of Computer Science field of study Applied Mathematics and Informatics; 2 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Mago-Lego; 1, 2 module)Rus
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Economic Sciences field of study Applied Mathematics and Information Science, Economics; 2 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Master’s programme; Faculty of Computer Science field of study Applied Mathematics and Informatics; 1 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Mago-Lego; 3, 4 module)Rus
Courses (2023/2024)
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Mago-Lego; 3, 4 module)Rus
- Fundamentals of Matrix Computations (Master’s programme; Faculty of Computer Science field of study Applied Mathematics and Informatics; 1 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Economic Sciences field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
Courses (2022/2023)
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
- Fundamentals of Matrix Computations (Master’s programme; Faculty of Computer Science field of study Applied Mathematics and Informatics; 1 year, 3, 4 module)Rus
Courses (2021/2022)
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
- Foundations of Tensor Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 1, 2 module)Rus
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
Courses (2020/2021)
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 2 year, 3, 4 module)Rus
- Matriх Computations (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 1, 2 module)Rus
Grants
2021 - present: PI for the grant 21-71-00119 (Russian Science Foundation), “Adaptive tensor methods for partial differential equations”.
2016 - 2017: PI for the grant 16-31-00372 (Russian Fund for Basic Research), “Fast tensor approach to electronic structure calculation”.
Recommender Systems: New Algorithms and Current Practices
The AI and Digital Science Institute at the HSE Faculty of Computer Science hosted a conference focused on cutting-edge recommender system technologies. In an atmosphere of active knowledge sharing among leading industry experts, participants were introduced to the latest advancements and practical solutions in recommender model development.
New Joint Department with RAS Marchuk Institute of Numerical Mathematics Opens at HSE University
A new joint department with the Marchuk Institute of Numerical Mathematics (INM RAS) has been established at the HSE Faculty of Computer Science. This collaboration promises to combine the scientific and educational resources of two leading research centres, enabling the training of highly qualified specialists. The department is headed by Dr Andrey Bogatyrev, a professor and Doctor of Physics and Mathematics.
The Secret to Success: How Recommender Systems Are Changing Industry
A scientific conference dedicated to the young and actively developing field of recommender systems was held at HSE University. Representatives of the scientific community and industry gathered at the site to exchange cutting-edge ideas and best practices, as well as discuss opportunities to implement new technologies in real-life business scenarios.
New Labs to Open at Faculty of Computer Science
Based on the results of a project competition, two new laboratories are opening at HSE University’s Faculty of Computer Science. The Laboratory for Matrix and Tensor Methods in Machine Learning will be headed by Maxim Rakhuba, Associate Professor at the Big Data and Information Retrieval School. The Laboratory for Cloud and Mobile Technologies will be headed by Dmitry Alexandrov, Professor at the School of Software Engineering.
17 Articles by Researchers of HSE Faculty of Computer Science Accepted at NeurIPS
In 2022, 17 articles by the researchers of HSE Faculty of Computer Science were accepted at the NeurIPS (Conference and Workshop on Neural Information Processing Systems), one of the world’s most prestigious events in the field of machine learning and artificial intelligence. The 36th conference will be held in a hybrid format from November 28th to December 9th in New Orleans (USA).
HSE Faculty of Computer Science and Skoltech Hold Math of Machine Learning Olympiad 2022
HSE's Faculty of Computer Science and the Skolkovo Institute of Science and Technology have held the Mathematics of Machine Learning Olympiad for the fifth time. The participants competed for prizes and the opportunity to matriculate at two universities without exams by enrolling in the HSE and Skoltech joint master's programme in Math of Machine Learning.