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Regular version of the site
Language Proficiency
+7 (342) 200-95-55
Address: Perm, 37а Gagarina Bulvar, room 218
SPIN-RSCI: 3825-0437
ORCID: 0000-0002-9317-8785
ResearcherID: E-4327-2014
Scopus AuthorID: 56018681500
Google Scholar
Office hours
Zoom by request, avbuzmakov@hse.ru, Thursday, 16:00 -- 18:00.
A. Kychkin
E. G. Plotnikova
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Aleksey V. Buzmakov

  • Aleksey V. Buzmakov has been at HSE University since 2015.

Education and Degrees

  • 2024
  • 2011

    Master's in Applied Mathematics and Physics
    Moscow Institute of Physics and Technology

  • 2009

    Bachelor's in Applied Mathematics and Physics
    Moscow Institute of Physics and Technology

Professional Interests

Courses (2021/2022)

Courses (2020/2021)

What is Data Science? (Master’s programme; Faculty of Social Sciences and Humanities (Perm); 1 year, 1-3 module)Rus

Courses (2019/2020)

Courses (2018/2019)

Materials and Resourses

DataCamp MOOC for interactive study of R, Python, Data analysis, and Machine Learing.

Dissertation for a degree of Candidate of Science


A. V. Buzmakov Моделирование процессов с состояниями сложной структуры на основе решёток замкнутых описаний







Article Alam M., Buzmakov A. V., Napoli A. Exploratory Knowledge Discovery over Web of Data // Discrete Applied Mathematics. 2018. Vol. 249. P. 2-17. doi








  • 2017
    IEEE International Conference on Data Mining (ICDM), 2017 (Новый Орлеан). Presentation: Efficient Mining of Subsample-Stable Graph Patterns
  • 2016
    The 13th International Conference on Concept Lattices and Their Applications (CLA2016) (Москва). Presentation: On Scaling of Fuzzy FCA to Pattern Structures
  • 2015
    13th International Conference on Formal Concept Analysis, ICFCA 2015 (Nerja). Presentation: Revisiting Pattern Structure Projection
  • Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015 (Porto). Presentation: Fast Generation of Best Interval Patterns for Nonmonotonic Constraint
  • 4th International Workshop "What can FCA do for Artificial Intelligence?", FCA4AI 2015 (Buenos Aires). Presentation: SOFIA: How to Make FCA Polynomial?


SPARC Project  of Ministry of Human Resource Development (Government of India)

IDLab has organized courses on the application of natural language processing (NLP) methods using Python

Natural language processing methods are one of the advanced machine learning methods and allow for the analysis and synthesis of text data. In particular, NLP methods solve problems such as classification, comparison and text generation. NLP methods are actively developing and their study is relevant both for fundamental research and for the implementation of applied projects.

IDLab study image analysis with PyTorch

On June 20, 2022, the advanced training course "Python Programming for Image Analysis" began. As part of the course, IDLab members will learn machine learning methods that can be used for image analysis.

IDLab Refresher Course: Analyzing Text Data in R and Python

Despite the summer holidays, IDLab members and trainees are undergoing advanced training in automated text data analysis

French Scientist Amedeo Napoli Gave Lectures for HSE-Perm Students

From 29 November to 1 December Amedeo Napoli, head of the Orpailleur research group, Laboratory of Lorraine, gave a number of lectures on description logic for students of “Business-informatics” and “Software engineering” educational programmes.

Welcome Aboard: Post-Doc Introductions

Every year, HSE hires post-doctoral researchers from all over the world. And in 2017-18, more than 30 of them started work at laboratories and research centres in a large range of fields and specializations. The HSE Lookis pleased to introduce this year’s international researchers, so that you can learn more about your colleagues and find out about opportunities for potential collaboration.

Alexey Buzmakov Presented Paper at Conference in Porto

On September 7-11 the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases was held in Porto, Portugal.