Collaboration with “Dubna” University

International IT-School “Data Science”

International IT-School “Data Science” of Dubna State University is a University organizational structure which provides IT-specialists’ training for the development of megascience project computing (NICA, PIC, LHC, FAIR, SKA, etc.), Data Science, digital economy, and other perspective directions.

The creation of International IT-School is a joint initiative between the Joint Institute for Nuclear Research (JINR) and Dubna State University taking into account the development of unique world-class scientific projects at JINR. Educational programs of International IT-School are based on personnel requirements of JINR and other high-technology economic organizations; moreover, they are realized with their help.

Bachelor’s and master’s theses

2018

  • Erofeeva Kseniya Sergeevna
    27.04.03 – System analysis and management
    Development of a complex of programs for studying the system of long Josephson junctions on hybrid computing architectures
    Master’s Work.
    Superviser: Dr. Streltsova O.I.
    Scientific adviser: Bashashin M.V.

2017

  • Butenko Yuri,
    Federal State Budgetary Educational Institution of Higher Education Dubna State University
    The problem of gamma-neutron separation for the DEMON spectrometer using machine learning methods.
    Supervisor: Prof., Korenkov V.V.
    Scientific advisers:PD. Dr. Streltsov A.I., Dr. Streltsova O.I.
    The goal of this paper is to research the applicability of machine learning methods to process and classify experimental data obtained from the DEMON detector and to perform a comparative analysis of the methods’ effectiveness as well. DEMON type detectors are one of the basic measuring elements for ATLAS and ALICE experiments usually used to detect neutrons and gamma quanta.
    The conventional software package for this detector allows to map all events into energy diagrams that are recorded and saved for later statistical and mathematical analysis. The main problem of statistical processing of the obtained data is to separate and identify which events correspond to neutrons or -quanta.
    The double integration method used in the conventional framework to solve the problem does not enable clear identification and separation of these events at certain energy intervals, that is a motivation to apply machine learning methods to this problem.
  • Savelyeva Ekaterina
    Software development for mobile neutron spectrometer
    Mastrer’s thesis, University of Dubna, ISAM
    Superviser: Dr. Streltsova O.I.
  • Kabiev Musa
    Estimation of microscopic optical potential of elastic scattering 6He + 12C in parallel mode 
    Bachelor’s thesis, University of Dubna, department of nuclear physics; Eurasian National University (Kazakhstan)
    Supervisor: Dr. Streltsova O.I.
    Scientific advisers: Dr. Zemlyanaya E. V.
  • Daribaev Ayzat
    Simulation of  DEMON neutron spectrometer efficiency calculation
    Bachelor’s thesis, University of Dubna, department of nuclear physics; Al-Farabi Kazakh National University
    Supervisor: Kozulin E. M.
    Scientific advisers: Dr. Streltsova O.I.
  • Mayorov A. V.
    Development and deployment of the web-service for experimental data analysis  on HPC platforms using ROOT package
    Master’ thesis, University of Dubna, ISAM
    Supervisor: Korenkov V. V.
    Scientific advisers: Dr. Streltsova O.I., Dr. Vala M.

 

2016

  • Ushakov S.
    Parallel algorithms for the tasks of multidimensional integration.  
    Bachelor’s thesis
    Superviser: Dr. Streltsova O.I.
  • Mayorov Aleksander
    Development of  a module for statistics gathering using web-sites for user activity monitoring. 
    Bachelor’s thesis
    Supervisor: Mikheev M.A.

Summer schools for students

Trainings for 2nd and 3d grade students of the Dubna University on Parallel Programming Technologies is held on the basis of the heterogeneous computing cluster HybriLIT.
The key aim is to learn parallel programming technologies that allow developing applications for carrying out computations using modern computing architectures.

Educational program on HPC technologies based on the heterogeneous cluster HybriLIT (LIT JINR)