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

Honours and Awards

(Sep. 2024) Poster Award in the Thematic Area of “PV in the Energy Transition” at the 41^st European Photovoltaic Solar Energy Conference and Exhibition.
(Dec. 2002) Thomaidio Award (NTUA) for having published a paper (during the final year of my undergraduate studies) in the internationally approved proceedings of the Scientific Conference: IEEE International Conference on Intelligent Robots and Systems, EPFL, Lausanne, Switzerland, October 2002.
(Sep. 1998) National Scholarship Foundation Award (Greece) for ranking among the top three students in the department of Mechanical Engineering at National Technical University of Athens.

Dr. Georgios Chasparis

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Research Interests

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Honours and Awards

Publikationen

2026

Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces

2025

Causally-Guided Pairwise Transformer - Towards Foundational Digital Twins in Process Industry

Automated Knowledge Graph Learning in Industrial Processes

Investigating the Potential of Latent Space for the Classification of Paint Defects

2024

Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies

Load Shifting in Energy Communities by Providing User-Centered Recommendations - Forecast, Optimization and Potential

Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry

Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach

PAS - A Feature Selection Process Definition for Industrial Settings

Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance

2023

Automated Cross Channel Temperature Predictions for the PFR Lime Kiln Operating Support

Predictive Modeling for Flexibility Load Forecasting in Prosumer Communities

Gathering Expert Knowledge in Process Industry

2022

Deep Residual Policy Reinforcement Learning as a Corrective Term in Process Control for Alarm Reduction: A Preliminary Report

Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing

Interpretable Input-Output Hidden Markov Model- Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines

Abstracting Process Mining Event Logs From Process-State Data To Monitor Control-Flow Of Industrial Manufacturing Processes

Generalized Input-Output Hidden-Markov-Models for Supervising Industrial Processes

2021

Feature and Model Selection for Day-ahead Electricity-Load Forecasting in Residential Buildings

Filter-based Feature Selection Methods for Industrial Sensor Data: A Review

AVUBDI: A Versatile Usable Big Data Infrastructure and its Monitoring Approaches for Process Industry

2020

Feature Extraction for Day-ahead Electricity-Load Forecasting in Residential Buildings

Reinforcement-Learning-based Optimization for Day-ahead Flexibility Extraction in Battery Pools

2019

A cooperative demand-response framework for day-ahead optimization in battery pools

Measurement-based efficient resource allocation with demand-side adjustments

A demand-response framework in balance groups through direct battery-storage control

Learning-based dynamic pinning of parallelized applications in many-core systems

Decentralized energy networks based on blockchain: Background, overview and concept discussion

Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games