Artem Ryzhikov
- 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
- Artem Ryzhikov has been at HSE University since 2017.
Education and Degrees
2018
Master'sHSE University
2015
Bachelor's in PhysicsNovosibirsk State 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.
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 (2025/2026)
- Generative Methods in Machine Learning (Bachelor’s programme; School of Computer Science, Physics and Technology field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Methods in Machine Learning (Bachelor’s programme; School of Computer Science, Physics and Technology field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Methods in Machine Learning (Bachelor’s programme; School of Computer Science, Physics and Technology field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Models in Machine Learning (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Models in Machine Learning (Optional course (faculty); Faculty of Computer Science; 3 module)Rus
- Generative Models in Machine Learning (advanced course) (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 3, 4 module)Rus
- Generative Models in Machine Learning (advanced course) (Optional course (faculty); Faculty of Computer Science; 3, 4 module)Rus
- Past Courses
Courses (2024/2025)
- Generative Models in Machine Learning (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Models in Machine Learning (advanced course) (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 3, 4 module)Rus
Courses (2023/2024)
- Generative Models in Machine Learning (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Models in Machine Learning (advanced course) (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 3 year, 3, 4 module)Rus
- Generative Models, Part 2 (Mago-Lego; 4 module)Rus
- Generative Models, Part 2 (Master’s programme; Faculty of Computer Science field of study Applied Mathematics and Informatics; 1 year, 4 module)Rus
- Machine Learning (Optional course (faculty); 3, 4 module)Rus
Courses (2022/2023)
- Generative Models in Machine Learning (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Models in Machine Learning (Mago-Lego; 3 module)Rus
- Generative Models in Machine Learning (Master’s programme; School of Computer Science, Physics and Technology field of study Applied Mathematics and Informatics, field of study Applied Mathematics and Informatics; 2 year, 3 module)Rus
Courses (2021/2022)
- Generative Models in Machine Learning (Bachelor’s programme; Faculty of Computer Science field of study Applied Mathematics and Information Science; 4 year, 3 module)Rus
- Generative Models in Machine Learning (Master’s programme; School of Computer Science, Physics and Technology field of study Applied Mathematics and Informatics; 1 year, 3 module)Rus
- Generative Models in Machine Learning (Master’s programme; School of Computer Science, Physics and Technology field of study Applied Mathematics and Informatics, field of study Applied Mathematics and Informatics; 2 year, 3 module)Rus
Dissertation for a Candidate of Sciences degree
- 2024
A. Ryzhikov Глубокие порождающие модели для поиска аномалий
Conferences
- 2021
ACAT 2021 (Daejeon). Presentation: Robust Neural Particle Identification Models
25
May
2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
6
May
2025
HSE Scientists Win Prestigious International Prize in Fundamental Physics
The 2025 Breakthrough Prize in Fundamental Physics has been awarded to the international collaborations of experiments at the Large Hadron Collider (LHC) at CERN, including the LHCb collaboration, in which researchers from HSE University have participated.