Title: What AI can do for control and what control can do for AI

Abstract: In recent years, learning-based methods have demonstrated significant success in controlling complex systems. These techniques involve using learning-based tools to either derive mathematical models of a system for synthesizing controllers or directly learn the controller itself. However, applying learning-based methods in safety-critical systems poses challenges, as these components are often regarded as black-box systems without formal guarantees. In this presentation, I will explore the synergy between machine learning and control theory by addressing two key areas: firstly, how learning enhances symbolic control methods. Secondly, I will explore strategies for overcoming optimality and safety constraints in reinforcement learning algorithms through the application of control techniques.