Dr. Georgios Chasparis

Key Researcher Data Science
Research Team Lead Scalable Optimization and Control


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Georgios Chasparis is a Key Researcher in Data Science conducting fundamental and applied research in predictive modeling, distributed optimization and control.


Research Interests

  • Predictive modeling: Designing of predictive modeling methods for complex systems with applications in industrial processes and smart grid.
  • Distributed optimization and control: Game-theoretic design for distributed optimization and decision making in the presence of computational and communication constraints, including scheduling and resource-allocation applications in networks.
  • Reinforcement learning: Advancing reinforcement learning as a online optimization method for industrial optimization problems.
  • Combinatorial optimization: Designing stochastic-based search methods for industrial combinatorial problems.


Key Facts

  • PhD studies at the department of Mechanical and Aerospace Engineering of the University of California Los Angeles CA (UCLA) with focus on distributed learning methods for uncertain and dynamic environments.
  • Post-doctoral research in the area of distributed optimization and control at the Georgia Institute of Technology, Atlanta GA, and Lund University, Sweden.
  • Project Manager in several national and international research projects in the area of energy management, smart-grid control and industrial process optimization, e.g., H2020 Cogniplant (#869931), FFG Flex+ (864996), H2020 CISC (Marie-Curie training network #955901), FFG ServeU (#881164)
  • Author of over 60 reviewed publications
  • Lecturer at Fachhochschule Oberösterreich Wels