Alexander Lepskiy
- Deputy Director:International Centre of Decision Choice and Analysis
- Professor:Faculty of Economic Sciences / Department of Mathematics
- Alexander Lepskiy has been at HSE University since 2009.
Education, Degrees and Academic Titles
- 2009
Doctor of Sciences* in Theoretical Foundations of Computer Science
Southern Federal University
Thesis Title: Probabilistic and possibilistic models of describing uncertainty in the problems of image processing and analysis - 1997Associate Professor
- 1993
Candidate of Sciences* (PhD)
- 1991
Doctoral programme in Mathematical analysis
Rostov-on-Don State University, Mechanics and Mathematics - 1986
Degree in Аpplied Mathematics
Taganrog State Radiotechnical University, Automation and Computer Science
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.
A post-doctoral degree called Doctor of Sciences is given to reflect second advanced research qualifications or higher doctorates in ISCED 2011.
Courses (2023/2024)
- Calculus-1 (Bachelor’s programme; Faculty of Economic Sciences; 1 year, 1, 2 module)Rus
- Calculus-2 (Bachelor’s programme; Faculty of Economic Sciences; 1 year, 3, 4 module)Rus
- Decision Making and Data Analysis under Uncertainty and Ambiguity (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Rus
Decision Making in the Problems of Digital Economics under Risk and Uncertainty (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 4 year, 2 module)Rus
Decision Making in the Problems of Digital Economics under Risk and Uncertainty (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 3 year, 2 module)Rus
- Quantitative Methods for Management Decisions (Bachelor’s programme; Graduate School of Business; 2 year, 1, 2 module)Rus
- Past Courses
Courses (2022/2023)
- Calculus (Bachelor’s programme; Faculty of Economic Sciences; 1 year, 1-4 module)Rus
- Decision Making and Data Analysis under Uncertainty and Ambiguity (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Rus
- Decision Making and Data Analysis under Uncertainty and Ambiguity (Mago-Lego; 1, 2 module)Rus
Decision Making in the Problems of Digital Economics under Risk and Uncertainty (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 4 year, 2 module)Rus
Decision Making in the Problems of Digital Economics under Risk and Uncertainty (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 3 year, 2 module)Rus
- Special chapters of decision theory (Bachelor’s programme; Graduate School of Business; 4 year, 3 module)Rus
Courses (2021/2022)
- Calculus (Bachelor’s programme; Faculty of Economic Sciences; 1 year, 1-4 module)Rus
- Decision Making and Data Analysis under Uncertainty and Ambiguity (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Rus
Decision Making in the Problems of Digital Economics under Risk and Uncertainty (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 3 year, 2 module)Rus
- Decision Making in the Problems of Digital Economics under Risk and Uncertainty (Bachelor’s programme; Faculty of Economic Sciences; 4 year, 2 module)Rus
Courses (2020/2021)
- Calculus (Bachelor’s programme; Faculty of Economic Sciences; 1 year, 1-4 module)Rus
- Decision Making and Data Analysis under Uncertainty and Ambiguity (Master’s programme; Faculty of Computer Science; 1 year, 1, 2 module)Rus
- Исследовательский проектный семинар (НИС) (Optional course (faculty); Faculty of Economic Sciences; 1-3 module)Rus
Conferences
- 2023Data Analysis, Optimization and Their Applications (Dolgoprudny, Moscow Region). Presentation: About Some Clustering Algorithms in Evidence Theory
- Intelligent and Fuzzy Systems (Стамбул). Presentation: Fuzzy Threshold Aggregation
- The Tenth International Conference on Information Technology and Quantitative Management (Оксфорд). Presentation: Evidence-Based Aggregation and Ranking in an Ordinal Scale
- 2022International Conference on Belief Functions (Париж). Presentation: Cluster Decomposition of the Body of Evidence
- International Conference on Information Technology and Quantitative Management (Пекин). Presentation: On Optimal Blurring of Point Expert Estimates and their Aggregation in the Framework of Evidence Theory
- 2021Belief Functions: Theory and Applications, 6th International Conference, BELIEF 2021 (Shanghai). Presentation: Conflict Measure of Belief Functions with Blurred Focal Elements on the Real Line
- 202018th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (Lisbon). Presentation: Belief Functions for the Importance Assessment in Multiplex Networks
- 2019
Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (Прага). Presentation: Application of Non-additive Measures and Integrals for Analysis of the Importance of Party Positions for Voting
Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (Прага). Presentation: Clustering a body of evidence based on conflict measures
- 7th International Conference on Information Technology and Quantitative Management - ITQM 2019 (Гранада). Presentation: Application of the Belief Function Theory to the Development of Trading Strategies
- 2018Совместная конференция The BELIEF and SMPS (Soft Methods in Probability and Statistics) conferences (Кoмпьень). Presentation: On the Conflict Measures Agreed with the Combining Rules
- 2017
The 10th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT-2017 (Варшава). Presentation: Aggregation of Forecasts and Recommendations of Financial Analysts in the Framework of Evidence Theory
5th International Conference on Information Technology and Quantitative Management (Нью-Дели). Presentation: Decomposition of Evidence and Internal Conflict
Publications37
- Chapter Lepskiy A. About Some Clustering Algorithms in Evidence Theory, in: Data Analysis and Optimization. In Honor of Boris Mirkin's 80th Birthday / Ed. by B. Goldengorin, S. Kuznetsov. Springer, 2023. doi Ch. 12. P. 179-190. doi
- Article Lepskiy A. Evidence-Based Aggregation and Ranking in an Ordinal Scale // Procedia Computer Science. 2023. Vol. 221. P. 1066-1073. doi
- Chapter Lepskiy A. Fuzzy Threshold Aggregation, in: Intelligent and Fuzzy Systems. Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference, Volume 1. Vol. 758. Issue 1. Cham : Springer, 2023. doi P. 69-76. doi
- Chapter Lepskiy A. Cluster Decomposition of the Body of Evidence, in: 7th International Conference, BELIEF 2022, Paris, France, October 26–28, 2022, Proceedings. Springer, 2022. doi Ch. 16. P. 163-173. doi
- Article Andrey G. Bronevich, Alexander E. Lepskiy. Measures of conflict, basic axioms and their application to the clusterization of a body of evidence // Fuzzy Sets and Systems. 2022. Vol. 446. P. 277-300. doi
- Article Lepskiy A. On Optimal Blurring of Point Expert Estimates and their Aggregation in the Framework of Evidence Theory // Procedia Computer Science. 2022. Vol. 214. P. 573-580. doi
- Chapter Lepskiy A. Conflict Measure of Belief Functions with Blurred Focal Elements on the Real Line, in: Belief Functions: Theory and Applications: 6th International Conference, BELIEF 2021, Shanghai, China, October 15–19, 2021, Proceedings Vol. 12915. Springer, 2021. doi P. 197-206. doi
- Chapter Lepskiy A., Meshcheryakova N. Belief Functions for the Importance Assessment in Multiplex Networks, in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020 Vol. 1238. Part 2. Cham : Springer, 2020. doi P. 283-296. doi
- Article Lepskiy A. Decompositional approach for evaluation of internal conflict in the framework of the evidence theory // Нечеткие системы и мягкие вычисления. 2020. Vol. 15. No. 1. P. 43-63. doi
- Chapter Lepskiy A., Smolev V. Application of Non-additive Measures and Integrals for Analysis of the Importance of Party Positions for Voting, in: Atlantis Studies in Uncertainty Modelling, Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) Vol. 1. Atlantis Press, 2019. P. 321-327. doi
- Article Lepskiy A., Suevalov A. Application of the Belief Function Theory to the Development of Trading Strategies // Procedia Computer Science. 2019. Vol. 162. P. 235-242. doi
- Chapter Bronevich A., Lepskiy A. Clustering a Body of Evidence Based on Conflict Measures, in: Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) Vol. 1. P. : Atlantis Press, 2019. P. 328-333. doi
- Chapter Lepskiy A., Кутынина Е. А. Aggregation of Forecasts and Recommendations of Financial Analysts in the Framework of Evidence Theory, in: Advances in Fuzzy Logic and Technology 2017 Vol. 642. Cham : Springer, 2018. doi P. 370-381. doi
- Chapter Lepskiy A., Suevalov A. Application of Fuzzy Asymmetric GARCH-Models to Forecasting of Volatility of Russian Stock Market, in: Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). Advances in Intelligent Systems and Computing Vol. 679. Cham : Springer, 2018. doi P. 286-294. doi
- Chapter Lepskiy A. On the Conflict Measures Agreed with the Combining Rules, in: Belief Functions: Theory and Applications 5th International Conference, BELIEF 2018, Compiègne, France, September 17-21, 2018, Proceedings Vol. 11069. Springer, 2018. doi P. 172-180. doi
- Article Lepskiy A. On the Preservation of Comparison of Distorted Histograms // International Journal of Information Technology and Decision Making. 2018. Vol. 17. No. 1. P. 339-355. doi
- Chapter Lepskiy A. Decomposition of Evidence and Internal Conflict, in: Procedia Computer Science Vol. 122: 5th International Conference on Information Technology and Quantitative Management, ITQM 2017. Elsevier, 2017. P. 186-193. doi
- Chapter Kutynina E., Lepskiy A. Dynamic Analysis of the Development of Scientific Communities in the Field of Soft Computing, in: Advances in Intelligent Systems and Computing Vol. 456: Soft Methods for Data Science. Springer, 2017. doi P. 293-302. doi
- Article Lepskiy A. Stochastic and fuzzy ordering with the method of minimal transformations // Automation and Remote Control. 2017. Vol. 78. No. 1. P. 50-66. doi
- Chapter Lepskiy A. The Qualitative Characteristics of Combining Evidence with Discounting, in: Advances in Intelligent Systems and Computing Vol. 456: Soft Methods for Data Science. Springer, 2017. doi P. 311-318. doi
- Chapter Lepskiy A. A Comparison of Stochastic and Fuzzy Orderings, in: Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16) Vol. 450. Issue 1. Springer, 2016. doi P. 27-37. doi
- Chapter Bronevich A., Lepskiy A., Penikas H. I. Coherence Analysis of Financial Analysts’ Recommendations in the Framework of Evidence Theory, in: SCAKD 2016 – The Second International Workshop on Soft Computing Applications and Knowledge Discovery. Proceedings of the Second International Workshop on Soft Computing Applications and Knowledge Discovery. July 18, 2016 / Ed. by M. Ojeda-Aciego, D. I. Ignatov, A. Lepskiy. Vol. 1687. CEUR Workshop Proceedings, 2016. P. 12-23.
- Chapter Lepskiy A. On internal conflict as an external conflict of a decomposition of evidence, in: Lecture Notes in Artificial Intelligence Vol. 9861: Belief Functions: Theory and Applications. Springer, 2016. doi P. 25-34. doi
- Article Lepskiy A., Bronevich A. Imprecision indices: axiomatic, properties and applications // International Journal of General Systems. 2015. Vol. 44. No. 7-8. P. 812-832. doi
- Chapter Lepskiy A. On Comparison of Distorted Histograms, in: Strengthening Links Between Data Analysis and Soft Computing Vol. 315. L., NY, Dordrecht, Heidelberg : Springer, 2015. P. 159-166.
- Article Bronevich A., Lepskiy A., Penikas H. I. The Application of Conflict Measure to Estimating Incoherence of Analyst's Forecasts about the Cost of Shares of Russian Companies // Procedia Computer Science. 2015. Vol. 55. P. 1113-1122.
- Chapter Bronevich A., Lepskiy A., Penikas H. I. The Application of Conflict Measure to Estimating Incoherence of Analyst's Forecasts about the Cost of Shares of Russian Companies, in: Procedia Computer Science. 3rd International Conference on Information Technology and Quantitative Management, ITQM 2015 Vol. Volume 55. Amsterdam : Elsevier, 2015. P. 1113-1122.
- Chapter Lepskiy A. General Schemes of Combining Rules and the Quality Characteristics of Combining, in: Belief Functions: Theory and Applications Vol. 8764. L., NY, Dordrecht, Heidelberg : Springer, 2014. P. 29-38.
- Chapter Lepskiy A. On the Stability of Comparing Histograms with Help of Probabilistic Methods, in: Procedia Computer Science. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. National Research University Higher School of Economics (HSE) in Moscow (Russia) on June 3-5, 2014 / Ed. by Y. Shi, A. Lepskiy, F. T. Aleskerov. Vol. 31. Amsterdam : Elsevier, 2014. P. 597-605.
- Book Procedia Computer Science. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. National Research University Higher School of Economics (HSE) in Moscow (Russia) on June 3-5, 2014 / Ed. by Y. Shi, A. Lepskiy, F. T. Aleskerov. Vol. 31. Amsterdam : Elsevier, 2014.
- Chapter Lepskiy A. About Relation between the Measure of Conflict and Decreasing of Ignorance in Theory of Evidence, in: Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13). P., Beijing, Amsterdam : Atlantis Press, 2013. P. 355-362.
- Article Lepskiy A. Determination of Symmetry Parameters of Objects in Noisy Images // Pattern Recognition and Image Analysis. 2013. Vol. 23. P. 408-414.
- Article Lepskiy A. Estimation of Conflict and Decreasing of Ignorance in Dempster-Shafer Theory // Procedia Computer Science. 2013. Vol. 17. P. 1113-1120. doi
- Chapter Bronevich A., Lepskiy A., Umansky V., Yakushev D. Recognition of Cross Profiles of Roadbed Based on Polygonal Representations, in: Proc. 5th International Conference Pattern Recognition and Machine Intelligence (PReMI'2013) Vol. LNCS 8251. Berlin, Heidelberg : Springer, 2013. P. 270-276.
- Chapter Lepskiy A. Stochastic Measure of Informativity and Its Application to the Task of Stable Extraction of Features, in: Synergies of Soft Computing and Statistics for Intelligent Data Analysis / Ed. by R. Kruse, M. R. Berthold, C. Moewes, M. Á. Gil, P. Grzegorzewski, O. Hryniewicz. Issue 190. L., NY, Dordrecht, Heidelberg : Springer, 2013. P. 553-561.
- Chapter Lepskiy A. Solution of Problem of Stable Extraction of Features with Using of Measure of Informativeness, in: Proceedings 2nd Stochastic Modeling Techniques and Data Analysis International Conference, 5 - 8 June 2012, Chania, Crete, Greece (SMTDA 2012) / Ed. by C. Skiadas. Chania : SMTDA, 2012. P. 489-496.
- Article Lepskiy A. Stable Feature Extraction with the Help of Stochastic Information Measure // Lecture Notes in Computer Science. 2011. No. 6744. P. 54-59.
Research interests
- theory of non-additive measure and its application
- imprecise probabilities
- pattern recognition
- image processing and image analyses
- mathematical model of study
Lecture by Professor A. Lepskiy on "Conflict of evidence evaluation within the framework of the theory of belief functions"
On Wednesday, February 21 the all-Russian seminar "Mathematical methods of decision analysis in economics, finance and politics" was held. Professor A. Lepskiy gave a lecture on «Conflict of evidence evaluation within the framework of the theory of the belief functions»