Aleksey V. Buzmakov
- Research Assistant:HSE Campus in Perm / International Laboratory of Intangible-driven Economy
- Aleksey V. Buzmakov has been at HSE University since 2015.
Education and Degrees
- 2015PhD
- 2015
Candidate of Sciences* (PhD)
HSE University - 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
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 (2021/2022)
- What is Data Science? (Master’s programme; Faculty of Economics; 1 year, 1-3 module)Rus
- Past Courses
Courses (2020/2021)
Materials and Resourses
DataCamp MOOC for interactive study of R, Python, Data analysis, and Machine Learing.
20222
- Article Makhalova T., Buzmakov A. V., Kuznetsov S., Napoli A. Introducing the closure structure and the GDPM algorithm for mining and understanding a tabular dataset // International Journal of Approximate Reasoning. 2022. Vol. 145. P. 75-90. doi
- Article Кулеш А., Куликова С. П., Дробаха В., Мехряков С., Бартули В., Бузмаков А. В., Сыромятникова Л., Собянин К. В., Каракулова Ю. Роль поражения островковой коры в определении патогенетического подтипа ишемического инсульта // Неврология, нейропсихиатрия, психосоматика. 2022. Т. 14. № 2. С. 11-17. doi
20214
- Chapter Buzmakov A. V., Kuznetsov S., Makhalova T., Napoli A. Exploring the dataset structure by means of delta-classes of equivalence. The case of the titanic dataset?, in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021) / Ed. by S. Kuznetsov, A. Napoli, S. Rudolph. Vol. 2972. CEUR-WS, 2021. Ch. 2. P. 19-26.
- Chapter Kulikova S., Buzmakov A. V. Finding the TMS-Targeted Group of Fibers Reconstructed from Diffusion MRI Data, in: Data Analytics and Management in Data Intensive Domains: 22nd International Conference, DAMDID/RCDL 2020, Voronezh, Russia, October 13–16, 2020, Selected Proceedings. Springer, 2021. P. 110-121. doi
- Book Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings / Ed. by A. Braud, Buzmakov Aleksey, T. Hanika, F. Le Ber. Springer, 2021. doi
- Book Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020 Revised Supplementary Proceedings / Ed. by W. M. van der Aalst, V. Batagelj, A. V. Buzmakov, D. I. Ignatov, A. A. Kalenkova, M. Khachay, O. Koltsova, A. Kutuzov, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch, I. Makarov, A. Napoli, A. Panchenko, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 12602. Springer, 2021. doi
20202
- Chapter Jyoti -., Buzmakov Aleksey, Kailasam S. Towards Stable Significant Subgroup Discovery, in: The 15th International Conference on Concept Lattices and Their Applications CLA2020 Issue 2668. CEUR-WS, 2020. P. 287-292.
- Chapter Buzmakov A. V. Towards polynomial subgroup discovery by means of FCA?, in: Eighth International Workshop “What can FCA do for Artificial Intelligence?” / Ed. by S. Kuznetsov, A. Napoli, S. Rudolph. , 2020. P. 57-68.
20194
- Book EEML 2019: Experimental Economics and Machine Learning: Proceedings of the Fifth Workshop on Experimental Economics and Machine Learning at the National Research University Higher School of Economics co-located with the Seventh International Conference on Applied Research in Economics (iCare7) / Отв. ред.: D. I. Ignatov, A. V. Buzmakov. CEUR Workshop Proceedings, 2019.
- Preprint Бузмаков А. В. Machine Learning for Subgroup Discovery under Treatment Effect / Cornell University. Серия math "arxiv.org". 2019. № arXiv:1902.10327.
- Book Proceedings of the Fifth International Workshop on Experimental Economics and Machine Learning (EEML 2019),Perm, Russia, September 26, 2019 / Ed. by A. V. Buzmakov, K. Heinrich, D. I. Ignatov, D. Potapov, R. Tagiew. Vol. 2479. CEUR Workshop Proceedings, 2019.
- Preprint Alexey Buzmakov, Daria Semenova, Maria Temirkaeva. The Comparison of Methods for Individual Treatment Effect Detection / Cornell University. Series Computer Science "arxiv.org". 2019. No. arXiv:1912.01443.
20181
20172
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. Efficient Mining of Subsample-Stable Graph Patterns, in: 2017 IEEE 17th International Conference on Data Mining (ICDM). New Orleans : IEEE, 2017. Ch. 89. P. 757-762. doi
- Chapter Naidenova X., Buzmakov A. V., Parkhomenko V., Schukin A. Notes on relation between symbolic classifiers, in: Formal Concept Analysis for Knowledge Discovery. Proceedings of International Workshop on Formal Concept Analysis for Knowledge Discovery (FCA4KD 2017), Moscow, Russia, June 1, 2017. / Ed. by S. Kuznetsov, B. W. Watson. Vol. 1921. CEUR-WS.org, 2017. P. 88-103.
20164
- Chapter Tang M. T., Buzmakov A. V., Toussaint Y., Napoli A. Building a Domain Knowledge Model based on a Concept Lattice Integrating Expert Constraints, in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings / Ed. by M. Huchard, S. Kuznetsov. Vol. 1624. M. : Higher School of Economics, National Research University, 2016. P. 349-362.
- Chapter Buzmakov A. V., Napoli A. How Fuzzy FCA and Pattern Structures are Connected?, in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016) / Ed. by Sergei O. Kuznetsov, Napoli Amedeo, S. Rudolph. M. : , 2016. P. 89-96.
- Chapter Buzmakov A. V., Napoli A. On Scaling of Fuzzy FCA to Pattern Structures?, in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings / Ed. by M. Huchard, S. Kuznetsov. Vol. 1624. M. : Higher School of Economics, National Research University, 2016. P. 85-96.
- Article Buzmakov A. V., Egho E., Jay N., Kuznetsov S., Napoli A., Raissi C. On mining complex sequential data by means of FCA and pattern structures // International Journal of General Systems. 2016. Vol. 45. No. 2. P. 135-159. doi
20157
- Article Metivier J., Lepailleur A., Buzmakov A. V., Poezevara G., Cremilleux B., Kuznetsov S., Goff J., Napoli A., Bureau R., Cuissart B. Discovering structural alerts for mutagenicity using stable emerging molecular patterns // Journal of Chemical Information and Modeling. 2015. Vol. 55. No. 5. P. 925-940. doi
- Chapter Leeuwenberg A., Buzmakov A. V., Toussaint Y., Napoli A. Exploring Pattern Structures of Syntactic Trees for Relation Extraction, in: Formal Concept Analysis. 13th International Conference, ICFCA 2015, Nerja, Spain, June 23-26, 2015, Proceedings Vol. 9113. Springer, 2015. P. 153-168.
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. Fast Generation of Best Interval Patterns for Nonmonotonic Constraints, in: Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings Vol. 9285. Part 2. L., NY, Dordrecht, Heidelberg, Cham : Springer, 2015. P. 157-172.
- Chapter Alam M., Buzmakov A. V., Codocedo V., Napoli A. Mining Definitions from RDF Annotations Using Formal Concept Analysis, in: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015. Palo Alto : AAAI Press, 2015. P. 823-829.
- Article Kulikova S., Hertz-Pannier L., Dehaene-Lambertz G., Buzmakov A. V., Poupon C., Dubois J. Multi-parametric evaluation of the white matter maturation // Brain Structure and Function. 2015. Vol. 220. No. 6. P. 3657-3672.
- Chapter Kaytoue M., Codocedo V., Buzmakov A. V., Baixeries J., Kuznetsov S., Napoli A. Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing, in: Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings Vol. 9286. Part III. L., NY, Dordrecht, Heidelberg, Cham : Springer, 2015. P. 227-231.
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. Revisiting pattern structure projections, in: Formal Concept Analysis. 13th International Conference, ICFCA 2015, Nerja, Spain, June 23-26, 2015, Proceedings Vol. 9113. Springer, 2015. P. 200-215.
20144
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. Concept Stability as a Tool for Pattern Selection, in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2014) / Ed. by S. Kuznetsov, A. Napoli, S. Rudolph. Vol. 1257. Prague : CEUR Workshop Proceedings, 2014. P. 51-58.
- Article Buzmakov A. V., Kuznetsov S., Napoli A. Is Concept Stability a Measure for Pattern Selection? // Procedia Computer Science. 2014. Vol. 31. P. 918-927. doi
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. On Evaluating Interestingness Measures for Closed Itemsets, in: STAIRS 2014. Proceedings of the 7th European Starting AI Researcher Symposium Vol. 264. IOS Press, 2014. P. 71-80.
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. Scalable Estimates of Concept Stability, in: Formal Concept Analysis. 12th International Conference, ICFCA 2014, Cluj-Napoca, Romania, June 10-13, 2014. Proceedings Vol. 8478. Springer, 2014. P. 157-172.
20134
- Chapter Buzmakov A. V., Egho E., Jay N., Kuznetsov S., Napoli A., Raissi C. FCA and pattern structures for mining care trajectories, in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013) / Ed. by S. Kuznetsov, A. Napoli, S. Rudolph. Issue 1058. Beijing : CEUR Workshop Proceedings, 2013. P. 7-14.
- Chapter Buzmakov A. V., Egho E., Jay N., Kuznetsov S., Napoli A., Raissi C. On Projections of Sequential Pattern Structures (with an application on care trajectories), in: CLA 2013 Proceedings of the Tenth International Conference on Concept Lattices and Their Applications. La Rochelle : Laboratory L3i, University of La Rochelle, 2013. P. 199-210.
- Chapter Buzmakov A., Neznanov A. Practical Computing with Pattern Structures in FCART Environment, in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013) / Ed. by S. Kuznetsov, A. Napoli, S. Rudolph. Issue 1058. Beijing : CEUR Workshop Proceedings, 2013. Ch. 7. P. 49-56.
- Article Бузмаков А. В. Узорные структуры для анализа сложных последовательностей // Научно-техническая информация. Серия 2: Информационные процессы и системы. 2013. № 10. С. 27-39.
20122
- Chapter Kuznetsov S., Buzmakov A. V., Asses Y., Bourquard T., Napoli A. A Hybrid Classification Approach based on FCA and Emerging Patterns – An application for the classification of biological inhibitors, in: CLA 2012: Proceedings of the 9th International Conference on Concept Lattices and Their Applications. Malaga : Universidad de Malaga, 2012. P. 211-222.
- Chapter Buzmakov A. V., Kuznetsov S., Napoli A. A New Approach to Classification by Means of Jumping Emerging Patterns, in: Proceedings, Workshop “What can FCA do for Artificial Intelligence?” of the ECAI 2012 conference. M. : CEUR Workshop Proceedings, 2012. P. 15-22.
Conferences
- 2017IEEE International Conference on Data Mining (ICDM), 2017 (Новый Орлеан). Presentation: Efficient Mining of Subsample-Stable Graph Patterns
- 2016The 13th International Conference on Concept Lattices and Their Applications (CLA2016) (Москва). Presentation: On Scaling of Fuzzy FCA to Pattern Structures
- 201513th 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?
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.