G.A. Ososkov Homepage

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Professor, Doctor of Science Degree in Physics and Mathematics, Present Principal Researcher of Joint Institute for Nuclear Research

Gennady Alexeevich Ososkov

Post address:Laboratory of Informational Technologies,

Joint Institute for Nuclear Research,

Dubna, Moscow region, 141980, Russia

Office Phone: +7 (496) 21-64622

Fax: +7 (496) 21-65145

E-mail address: [email protected]

Education and Carrier:

  • Honors Diploma (M.Ph.), Moscow State University. Awarded in 1953
  • Ph.D. in mathematics, Moscow State University, under the supervision of Acad. A. N. Kolmogorov. Awarded in 1957
  • Doctor of Science Degree in physics and mathematics, Joint Institute for Nuclear Research. Awarded in 1987
  • Professor of Computational Statistics, Ivanovo State university, Russia. Awarded in 1991

Professional Positions:

  • 1953 - 1956 - postgraduate study in Moscow State University
  • 1956 - 1961 - engineer, scientist, senior scientist in the Special Research Institute of Electronic Industry at Moscow, USSR
  • Since 1961 an employee of Joint Institute for Nuclear research, Dubna,Russia:
  • 1961 - 1966 senior scientist - group leader,
  • 1966 - 1995 Head of computational statistics group
  • 1995 - present Principal researcher
  • 1988 - 2002 Professor of computational statistics at the Ivanovo State University
  • 2000 - present Professor of the Dubna, International University of nature, society and man
  • 2000 - 2015 Professor of the Dubna Branch of the Moscow State Technical University for Electronics and Automation

Awards and prizes for scientific work:

  • 1991 Silver medal of Slovak Academia of Sciences
  • 1997 The first JINR prize for the cycle of works devoted to artificial neural network applications
  • 2003-2004 Russian Federation state scientific stipend
  • 2011 Gold medal of Slovak Academia of Sciences
  • 2011 Honorable doctor degree of the Mongolian State University, Ulan-Bator
  • 2012 Jubilee medal in honour of 70th anniversary of the Mongolian State University
  • 2014 Honorary Diploma of the "Dubna" University
  • 2016 Honorary Diploma for merits to JINR
  • Gratitude of the Moscow Region Governor
  • Honorary title of "Honored Scientist of the Moscow Region"
  • The member of the editor board of the “Neuroinformatics” journal

Head of research projects

  • 2014-2015 Structure disign and improvement software development based on the synthesis of the modeling and monitoring processes for big data processing and storage systems, RFBR Grant 14-07-00215
  • 2015-2017 Development of the firmware complex for numerical researches of Josephson nanostructures based on the cloud computing center LIT JINR using parallel computing, RFBR Grant 15-29-01217

Area of expertise:

    mathematical statistics, pattern recognition, neural networks, wavelet analysis, simulations, cloud computing, data storage, optimization

Main publications from 01.01.2012

Other publications:

  • G.Ososkov,
    Robust tracking by cellular automata and neural network with non-local weights, in Applications and Science of Artificial Neural Networks, S. K. Rogers, D. W. Ruck, Editors, Proc. SPIE 2492, 1180 (1995).

  • N.Chernov, E.Kolganova, G.Ososkov,
    Optimal weights for circle fittind with discrete granular data. Preprint E-10-95-468 JINR, Report on PC'96 Conference

  • N.Chernov, E.Kolganova, G.Ososkov
    Robast Methods for the RICH Ring Recognition and Particle Identification. submitted to NIM, Report on PC'98 Conference

  • G.Ososkov
    Novel approch in RICH data handling, Czech. J. Phys. v. 49/S2 (1999) 145-160 Report on Symmetry & Spin'98 Conference

  • B.Lasiuk, D.Lyons, G.Ososkov, T.Ullrich
    Develeopment of an Elastic Tracking Package, Report on CHEP'98 Conference

  • A. Linka, G. Ososkov, J. Picek, P. Volf
    MCMC solution to circle fitting problem in analysis of RICH detector data, Czech. J. Phys.v. 49/S2 (1999) , 161-168. Report on COMPSTAT'98 Conference

  • G.Ososkov, V.Palichik, Y.Potrebennikov, G.Tatishvili, V.Shepelev
    Neural Network Applications for Efficiency Improving of Geometric Reconstruction of Events Detected in the EXCHARM Experiment at the JINR, Report on AIHENP'99 Workshop

  • G.Ososkov, A.Shitov
    Gaussian Wavelet Features and Their Applications for Analysis of Discretized Signals, Report on MTCP'98 Conference

  • N.Chernov, G.Ososkov, I.Silin
    ROBUST FITTING OF ELLIPSES TO NON-COMPLETE AND CONTAMINATED DATA, Report on the conference "SPIN and Symmetry" PRAGUE 1999,September

  • G.Ososkov, I.Puzynin, A.Polyanskij
    Modern methods of data processing in high energy physics experiments, "Particles and Nucleus", vol.33 (2002), #3 (russian)
    Abstract: In the given survey three basic methods of experimental data processing are considered which are intensively used during last decade in the Joint Institute for Nuclear Research, namely: robust methods of mathematical statistics, artificial neural networks, cellular automata and wavelet analysis. The main source of surveyed papers is those that have been elaborated by authors from Laboratory of Informational Technologies, as well as works developed in collaborations with famous physical centers as CERN, DESY, BNL etc. The authors describe the basic principles of discussed methods and gave most useful and promising examples of their applications.

  • G.Ososkov, A.Stadnik
    Face recognition by a new type of neural networks. in: "Advances in Neural Networks and Applications", Ed. N.Mastorakis, WSES Press (2001) 304-308.

  • M.Stehlik, G.Ososkov
    Efficient testing of the homogeneity, scale parameters and number of components in the Rayleigh mixture in: JINR Comm. E11-2003-116

  • S.Dmitrievsky, Yu.Gornushkin, G.Ososkov
    Neural Networks, Cellular Automata, and Robust Approach Applications for Vertex Localization in the OPERA Target Tracker in: JINR Comm. E10-2005-216

  • G.Ososkov
    Contemporary Methods of Data Processing in Experimental Physics in: Report on DataAnalysisMMCP-2006 Conference, High Tatra Mountains (Slovakia), August 28 - September 1, 2006
  • Ososkov G.A., Novel approaches of data-mining in experimental physics, Tatra Mountain Mathematical Publications, 51, 131-140, 2012, Pub:Mathematical Institute Slovak Academy of Science, Bratislava, Miloslav Duchon, ISSN 1210 – 3195. DOI: 10.2478/v10127-012-0013-0 https://www.sav.sk/journals/uploads/0912153413ososko.pdf

  • M. Stehlik, J. Kiselak and G. A. Ososkov, I-divergence based testing with applications to reliability and physics, MATHEMATICS IN ENGINEERING, SCIENCE AND AEROSPACE, 2012, 3, 4, 417- 432, Publishing:Cambrige Scientific Publishers, A.V. Balakrishnan, ISSN: 2041-3165. http://umv.science.upjs.sk/analyza/texty/clanky/JK_MESA12.pdf

  • Lebedev S., Hohne C., Kisel I., Lebedev A., Ososkov G., Algorithms and Software for Event Reconstruction in the RICH, TRD and MUCH Detectors of the CBM Experiment, Mathematical Modeling and Computational Science, Lecture Notes in Computer Science, v.7125, 2012, 246-251, Publishing:Springer. DOI: 10.1007/978-3-642-28212-6_28 http://link.springer.com/chapter/10.1007%2F978-3-642-28212-6_28#page-1

  • Kh. U. Abraamyan, S. A. Lebedev, G. A. Ososkov, A. N. Sissakian, A.S.Sorin, V. D. Toneev et al, RESONANCE STRUCTURE IN THE gg INVARIANT MASS SPECTRUM IN pC, dC, AND dCu INTERACTIONS, Nuclear physics, 75, 6, 707-710, 2012, Pub: Science. http://theor.jinr.ru/cpod/CPOD_Proc/Abraamyan_cpod10.pdf

  • V. Korenkov, A.Nechaevskiy, G. Ososkov, D. Pryahina, V. Trofimov, A. Uzhinskiy Grid-cloud services simulation for NICA project, as a mean of the efficiency increasing of their development. Computer Research and Modeling, 2014, vol. 6, no. 5, pp. 635–642 (Russian). ISSN: 2076-7633. http://crmen.ics.org.ru/journal/article/2185/

  • V. Korenkov, A.Nechaevskiy, G. Ososkov, D. Pryahina, V. Trofimov, A. Uzhinskiy Grid and cloud services simulation as an important step of their development. Systems and Means of Informatics. 2015. – Т.25, N1. — С. 3-19 DOI: 10.14357/08696527150101 http://www.ipiran.ru/journal/collected/2015_25_01_rus/

  • V. Korenkov, A.Nechaevskiy, G. Ososkov, D. Pryahina, V. Trofimov, A. Uzhinskiy Synthesis of the simulation and monitoring processes for the development of big data storage and processing facilities in physical experiments. Computer Research and Modeling, 2015, vol. 7, no. 3, pp. 691-698. http://crmen.ics.org.ru/journal/article/2325/