Research group

Machine Learning and Applications Group

The Machine Learning and Applications group is focused on the development of the application of state-of-the-art Machine Learning techniques in a multitude of various problems.

Main research projects

  • Deep Learning base-calling for nanopore sequencing
  • Design and optimization of computer models of intracellular mechanisms
  • Legal AI consulting
  • Machine Learning assistant for software engineering



  • T. Bryskin, A. Shpilman, D. Kudenko
    AAAI Workshop on Natural Language Processing for Software Engineering,
  • A. Gaydashenko, D. Kudenko, A. Shpilman
    A comparative evaluation of machine learning methods for robot navigation through human crowds
  • Timofey Bryksin, Evgenii Novozhilov, and Aleksei Shpilman
    Automatic Detection of Move Method Refactorings using Clustering Ensembles
    ASE Workshops,
  • 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,
  • A. Malysheva, D. Kudenko, A. Shpilman
    Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
  • I. Sosin, D. Kudenko, A. Shpilman
    Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
  • A. Shpilman, D. Boikiy, M. Plyakova, D. Kudenko, A. Burakov, E. Nadezhdina
    Sixteenth International Conference on Machine Learning and Applications (ICMLA),