Publications

• Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
  Framework for Deep Reinforcement Learning with GPU-CPU Multiprocessing
  Open Access Software, December 2018
• A. Gaydashenko, D. Kudenko, A. Shpilman
  A comparative evaluation of machine learning methods for robot navigation through human crowds
  ICMLA, December 2018
• Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman
  Deep Multi-Agent Reinforcement Learning with Relevance Graphs
  NeurIPS Deep Reinforcement Learning Workshop, December 2018
• A. Malysheva, D. Kudenko, A. Shpilman
  Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
  ICARCV, November 2018
• I. Sosin, D. Kudenko, A. Shpilman
  Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
  ICARCV, November 2018
• Timofey Bryksin, Evgenii Novozhilov, and Aleksei Shpilman
  Automatic Recommendation of Move Method Refactorings Using Clustering Ensembles
  ASE Workshops, September 2018
• A. Malysheva, A. Shpilman, D. Kudenko
  Learning to Run with Reward Shaping from Video Data
  Workshop on Adaptive and Learning Agents (ALA) at ICML-AAMAS, July 2018
• T. Bryskin, A. Shpilman, D. Kudenko
  Automated refactoring of object-oriented code using clustering ensembles
  AAAI Workshop on Natural Language Processing for Software Engineering, February 2018
• A. Shpilman, D. Boikiy, M. Plyakova, D. Kudenko, A. Burakov, E. Nadezhdina
  Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
  ICMLA, December 2017
• Shpilman A.A., Chudinova E.V., Lubitelev A., Ivanov P.A. Nadezhdina E.S.
  Investigation on Dependence of Stress Granules Formation on Microtubule Network Parameters and Stress Agent Concentration Through Numerical Model
  V ICMBB, 2014
• Shpilman A., Shudinova E., Ivanov P., Nadezhdina E.
  Dependence of stress granule formation on microtubules supports the idea of stress granule specific “glue” arising in stress conditions
  54th Annual Meeting of the American Society for Cell Biology, 2014
• Primako E.M., Shpilman A.A.
  Using Quazi-3D Approach for Microtubule Self-Organization Model
  V ICMBB, 2014
• Chudinova E.M., Shpilman A.A., Lubitelev A.V., Ivanov P.A., Nadezhdina E.S.
  Role of Microtubules in Formation of Stress Granules
  AIPRC, 2013
• Brodsky I.B., Burakov A.V., Zhapparova O.N., Ivanov P.A., Fokin A.I., Chudinova E.M., Shanina A.N., Shpilman A.A., Nadezhdina E.S.
  Molecules that Organize Cell Microtubule Systems and Why are They Doing It
  III SCBM, 2012
• Nadezhdina E.S., Lomakin A.J., Shpilman A.A., Chudinova E.M., Ivanov P.A.
  Microtubules govern stress granule mobility and dynamics, Biochimia Biophysica Acta
  Biochimia Biophysica Acta, 2010
• Shpilman A.
  Simple Computer Model Shows Posibility of Non-Motor Microtubule Binded Gradient Transport
  49th Annual Meeting of the American Society for Cell Biology, 2009
• Shpilman A.A, Nadezhdina E.S.
  In silico vs in vitro: Mimitation v1.0 program allows to predic morphological and dynamic changes of tubulin cytoskeleton caused by microtubule stabilization
  Biological Motility: Achievements and Perspectives, 2008
• E.V. Usova, A.V.Burakov, A.A. Shpilman, E.S. Nadezhdina
  Disturbance of the Radial System of Interphase Microtubules in the Presence of Excess Serum in Cell Culture Medium
  Biophysics, 2008
• Shpilman A., Nadezhdina E.S.
  Imitational Modeling of Cytoskeleton: Dynamics, Differentiation, Active Transport and Membrane Interactions
  48th Annual Meeting of the American Society for Cell Biology, 2008
• Shpilman A.A.
  Stochastic Modeling of Microtubule Systems
  I ICMBB, 2006
• А.А. Shpil’man and E.S. Nadezhdina
  Stochastic Computer Model of the Cell Microtubule Dynamics
  Biophysics, 2006
• Shpilman A.A., Nadezhdina E.S.
  Computational Stochastic Modeling of Cellular Microtubule Network
  46th Annual Meeting of the American Society for Cell Biology, 2006