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Language Proficiency
English
French
Hebrew
Contacts
Phone:
+7 (495) 621 13 42
+7 (495)772 95 90*26068 или 26065
E-mail:
Download CV (PDF, 318 Kb)
ORCID: 0000-0001-5195-9666
ResearcherID: L-1357-2015
Scopus AuthorID: 7801333355
Supervisor
F. T. Aleskerov
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Alexander Rubchinskiy

  • Alexander Rubchinskiy has been at HSE since 2007.

Education, Degrees and Academic Titles

  • 1990
    Associate Professor
  • 1977

    Candidate of Sciences* (PhD) in System Analysis, Management and Information Processing
    RAS Trapeznikov Institute of Control Sciences

  • 1968

    Degree
    Lomonosov Moscow 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.

Education

1968  - master degree in Mathematics at Moscow State University, School of Mechanics and Mathematics

1977 - Ph.D in Applied Math at Institute of Control Science of USSR Academy of Sciences

Courses (2013/2014)

Courses (2012/2013)

  • Mathematics of Conflicts (Master’s programme; Faculty of Public Administration; 2 year, 1, 2 module)Rus
  • Modelling and Сontrol (Bachelor’s programme; Faculty of Public Administration; spec. "Public Administration"; 3 year, 3, 4 module)Rus

Courses (2011/2012)

  • Individual and Social Decision Making (Master’s programme; School of Applied Mathematics and Information Science; spec. "Анализ и принятие решений"; 1 year, 3, 4 module)Rus
  • Mathematics of Conflicts (Master’s programme; Faculty of Public Administration; 2 year, 1, 2 module)Rus
  • Modelling and Сontrol (Bachelor’s programme; Faculty of Public Administration; spec. "Public Administration"; 3 year, 3, 4 module)Rus

Courses (2010/2011)

Publications17


Current research

  • Examination of the Brams-Taylor model of fair division in the case of an arbitrary number of players
  • Experimental examination of the developed classification algorithm involving a real data set in order to evaluate the dynamics of analyzed processes.
  • Applying methods of artificial intelligence to generalize from local analysis to global analysis of certain image classes