Konstantinos Gatsis

Science Oxford, University of Southampton

Papers

Total Citations

H-Index

About

Konstantinos Gatsis is a leading researcher at the intersection of control theory, reinforcement learning, and networked systems. His work addresses fundamental challenges in ensuring safety and stability when deploying learning-based controllers in real-world cyber-physical systems. Gatsis is best known for pioneering the integration of control-theoretic guarantees into reinforcement learning frameworks, most notably through his highly cited work on the Barrier-Lyapunov Actor-Critic approach (2023, 13 citations), which provides formal safety and stability certificates for RL agents. He has also made significant contributions to the field of wireless control systems, co-authoring a comprehensive retrospective (2024, 7 citations) that charts the evolution of wireless networked control and its impact on emerging applications like autonomous vehicles, robot swarms, and smart infrastructure. His research bridges the gap between rigorous control theory and modern machine learning, offering principled methods for deploying AI in safety-critical environments. Gatsis’s work continues to shape how researchers think about reliable autonomy in uncertain, communication-constrained settings.