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Viktor Yakovlev

  • Viktor Yakovlev has been at HSE since 2012.

Education and Degrees

  • 2012

    Candidate of Sciences* (PhD) in Mathematical Biology, Bioinformatics
    Институт проблем передачи информации им. А.А.Харкевича РАН

  • 2011

    Doctoral programme
    Институт Математических проблем биологии

  • 2008

    Master's
    Puschino State University

  • 2006

    Bachelor's
    Krasnoyarsk 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.

Student Term / Thesis Papers

Full list of of student term / thesis papers

Courses (2018/2019)

Courses (2017/2018)

Courses (2016/2017)

Courses (2015/2016)

Courses (2014/2015)

Courses (2013/2014)

Courses (2012/2013)

  • Analysis of Symbolic Sequences (Master’s programme; School of Applied Mathematics and Information Science; spec. "Анализ интернет-данных"; 2 year, 1, 2 module)Rus
  • Research Seminar "Internet Data Analysis" (Master’s programme; School of Applied Mathematics and Information Science; spec. "Анализ интернет-данных"; 2 year, 1-4 module)Rus

Publications9


Conferences

  • 2015

    Десятая конференция "Свободное программное обеспечение в высшей школе" (Переславль). Presentation: IDE для изучения Python

  • 2014

    Девятая конференция "Свободное программное обеспечение в высшей школе" (Переславль). Presentation: Ускорение выполнения Кумир-программ с помощью LLVM

  • 5th International Conference on Information, Intelligence, Systems and Applications, IISA 2014 (Chania, Crete). Presentation: Wire-tap channel concept in nature and society.
  • 2012
    6th International Conference on Mathematical Methods, Models and Architectures for Computer Network Security, MMM-ACNS 2012 (St. Petersburg). Presentation: Optimization of Key Distribution Protocols Based on Extractors for Noisy Channels within Active Adversaries.