The field of control systems is moving towards the development of safe and efficient methods for navigating dynamic environments. Researchers are introducing innovative approaches to address the challenges of ensuring safety and performance in systems with uncertainties and disturbances. One of the key directions is the use of control barrier functions (CBFs) and model predictive control (MPC) to guarantee safety and stability. Another important area is the development of risk-sensitive and distributionally robust methods for making decisions under uncertainty. Noteworthy papers in this area include the Distributionally Robust Acceleration Control Barrier Filter, which achieves efficient UAV obstacle avoidance through a novel control barrier function, and Provably Safe Model Updates, which introduces a framework for certifying the safety of model updates. Other notable works, such as the Dynamic Log-Gaussian Process Control Barrier Function and Safety Reinforced Model Predictive Control, demonstrate significant improvements in obstacle avoidance performance and safety margins. Overall, these advances are enabling the development of more reliable and efficient control systems for a wide range of applications, including autonomous driving and robotics.