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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