The field of control theory is moving towards the development of more robust and efficient methods for stability analysis and safety verification. Recent research has focused on the use of neural networks and machine learning techniques to improve the accuracy and scalability of these methods. One notable direction is the use of neural-network-based Lyapunov functions to estimate the region of attraction for nonlinear systems, which has shown significant reductions in conservatism compared to traditional methods. Another area of research is the development of new frameworks for robust verification of controllers under state uncertainty, such as the use of Hamilton-Jacobi reachability analysis. These advances have the potential to improve the safety and performance of complex systems, such as autonomous vehicles and robots. Noteworthy papers include: Region of Attraction Estimate Learning and Verification for Nonlinear Systems using Neural-Network-based Lyapunov Functions, which proposes a data-driven framework for learning and verifying RoA estimates. Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis, which introduces a framework for the robust verification of perception-based systems. PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles, which presents a novel pipeline for runtime geofencing that integrates a physics-encoded neural network with a discrete control barrier function.