Alexander Kuleshov
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S     Russian version

Alexander Kuleshov

Deputy Editor-in-Chief

Full Member of the Russian Academy of Sciences; Doctor of Technical Science; Professor; Director, A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences (IITP RAS); Head of the inter-departmental site-based Information Transmission and Data Analysis Problems Chair of the Moscow Institute of Physics and Technology (MIPT) at IITP RAS (Radio Engineering & Cybernetics and Management & Applied Mathematics Departments); Head of the IITP Complex System Modeling Technologies Chair at the Computer Science Department of the Higher School of Economics, Moscow, Russia.

Professional Membership:

  • Deputy Editor-in-Chief of Journal "Automation and Remote Control", Member of the Editorial Committee and Associate Editor of Journal "Problems of Informatics", Member of the Publishing Council of International Journal "Management Theory and Practices";
  • Vice Chairman, Department of Nano and Information Technologies, RAS;
  • Member of the RAS Coordination Council for Innovations and Intellectual Property;
  • Member of the RAS Council for Communications with Expatriate Russian Scientists;
  • Member of the RAS Scientific Publishing Board;
  • Member of the RAS Expert Committee for Awarding the Popov Gold Medal;
  • Member of the Board of Trustees, Skolkovo Institute of Science and Technology;
  • Member of the Scientific Advisory Council of the Skolkovo Foundation;
  • Chairman of the IITP Thesis Examination board.
Publications: 4 monographs, over 100 scientific publications.

Awards and Honors:
  • 1981 – Medal of Honor for Labor Valor.
Areas of Interest:
  • Information Technologies;
  • Mathematical Modeling.
Personal pages:

Selected Publications:
  • Mizin I.A., Bogatyrev V.A., Kuleshov A.P. Packet switching networks. - M.: Radio i sviaz, 1986. – 408 p.
  • Kuleshov A.P., Bernstein A.V., Burnaev Ye.V., Knowledge models in predictive metamodeling // Proc. of the International Research and Technology Conference “Information Technologies and Mathematical Modeling of Systems” (ITMM, 19-26 September 2010, France). Ì.: Institution of the Russian Academy of Sciences – Center for information technologies in the design, RAS, 2010. P. 209-210.
  • Kuleshov A.P., Bernstein A.V., Yanovich Yu.A. Dimensionality Reduction as Manifold estimation: asymptotically optimal algorithm // Proc. of 30th International Conference on Machine Learning (ICML-2013), Atlanta, USA.
  • Bernstein A.V., Kuleshov A.P., Manifold Learning: generalizing ability and tangential proximity // International Journal of Software and Informatics. 2013. Vol. 7, Issue 3, pp. 359-390.
  • Kuleshov A.P., Bernstein A.V., Yanovich Y.A., Asymptotically optimal method in Manifold estimation // Laszlo Markus, Vilmos Prokaj, eds. Abstracts of the XXIX-th European Meeting of Statisticians, 20-25 July 2013, Budapest, Hungary, 2013, p. 325.
  • Kuleshov A., Bernstein A., Manifold Learning in Data Mining Tasks. Machine Learning and Data Mining in Pattern Recognition // Proceedings of the 10th International Conference on Machine Learning and Data Mining (MLDM 2014). Series: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence. Springer, 2014. 15 p.
  • Bernstein A., Kuleshov A., Data-based Manifold Reconstruction via Tangent Bundle Manifold Learning // 31st International Conference on Machine Learning, Workshop “Topological Methods for Machine Learning”, Beijing, China, 25 of June, 2014. URL:
  • Bernstein A.V., Kuleshov A.P., Dimensionality Reduction in Statistical Learning // Proc. of the 13th International IEEE Conference on Machine Learning and Applications (ICMLA-2014), IEEE Computer Society. 2014, pp. 330-335.
  • Bernstein A.V., Kuleshov A.P., Low-Dimensional Data Representation in Data Analysis // Lecture Notes in Artificial Intelligence. Vol. 8774 “Artificial Neural Networks in Pattern Recognition”, Springer International Publishing, Switzerland, 2014. Pp. 47-58.
  • Bernstein A.V., Kuleshov, A.P., Yanovich Yu.A., Manifold Learning in Regression tasks. Lecture Notes in Artificial Intelligence. Vol. 9407 “Statistical Learning and Data Sciences”, Springer International Publishing, Switzerland, 2015. Pp. 414-423.

ICS RAS 2016