Dr. Reveliotis’ primary research interest has been in the area of Discrete Event Systems (DES) theory and its applications, especially in the management of flexibly automated workflows and the coordination of multi-agent traffic that evolves over graphs.

His work has adapted generic formal models of DES theory to the aforementioned applications, and has extended these models and the corresponding results towards the development of formal control frameworks able to control the dynamics of the underlying plant systems for, both, (i) behavioral correctness and robustness, and (ii) operational efficiency.

An additional element that is prominent in his work is a strong focus on the complexity of the addressed control problems and the tractability of the derived solutions. The latter is frequently established through a systematic and controllable trade-off between the operational efficiency of the targeted solution and its computational complexity.

Dr. Reveliotis has also a research interest in machine learning and its applications. Some of his past work in this area concerns the application of reinforcement learning theory in the context of optimal disassembly planning, and the efficient PAC learning of optimized control policies for processes that evolve over acyclic digraphs. He also has an active interest in grammatical inference and the potential of this theory in the DES modeling frameworks.

His teaching activity focuses on topics related to: (i) applied operations research, (ii) production planning and control, and (iii) the supervisory control of Discrete Event Systems.