Jiří Kubalík, Ph.D., born 1971

Czech Technical University in Prague
Czech Institute of Informatics, Cybernetics, and Robotics (CIIRC)
Jugoslávských partyzánu 1580/3, 160 00 Prague 6, Czech Republic
Mobile: +420 728 021 812
E-mail: jiri.kubalik@cvut.cz

Bio: I am a researcher at the Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague. I received the M.Sc. degree in computer science and the Ph.D. degree in artificial intelligence and biocybernetics from Czech Technical University (CTU) in Prague, in 1994 and 2001, respectively.
My research has mainly focused on various evolutionary computation techniques and their applications to complex optimization problems. Currently, my interests include a design of efficient symbolic regression techniques using prior knowledge to construct nonlinear models of dynamic systems and applications of machine learning techniques to flexible production systems.

International experience:
  • Vienna University of Technology, Austria — Research stay (2007, 1 year)
  • Delft University of Technology, The Netherlands — Post-doctoral stay (2001, 1 year)
  • Milwaukee School of Engineering & Rockwell Automation, Inc., Milwaukee, Wisconsin, USA — Internship (1996, 1 month)
  • University of Buckingham, UK — Internship (1995, 1 month)
  • University of Essen, Germany — Internship (1994, 1 month)

Current projects:

Recent publications:

  • Derner, E., Kubalík, J., & Babuška, R. (2021). Guiding Robot Model Construction with Prior Features. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7112–7118, Prague, Czech Republic.
  • Kubalík, J., Derner, E., & Babuška, R. (2021). Multi-Objective Symbolic Regression for Physics-Aware Dynamic Modeling. Expert Systems with Applications (182), November 2021, 115210.
  • Kubalík, J., Derner, E., Žegklitz, J., & Babuška, R. (2021). Symbolic Regression Methods for Reinforcement Learning. IEEE Access (9), October 2021, 139697–139711.
  • Derner, E., Kubalík, J., & Babuška, R. (2021). Selecting Informative Data Samples for Model Learning Through Symbolic Regression. IEEE Access (9), January 2021, 14148–14158.
  • Kubalík, J., Derner, E., & Babuška, R. (2020). Symbolic Regression Driven by Training Data and Prior Knowledge. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '20), 958–966, Association for Computing Machinery, New York, NY, USA.
  • Kulich, M., Kubalík, J., Přeučil, L. (2019). An Integrated Approach to Goal Selection in Mobile Robot Exploration. Sensors 2019, 19, 1400. https://doi.org/10.3390/s19061400
  • Kubalík, J., Kadera, P., Jirkovský, V., Kurilla, L., Prokop, S. (2019). Plant Layout Optimization Using Evolutionary Algorithms. HoloMAS 2019: 173-188

  Last update: 14. 11. 2022