The field of dynamical systems and control is moving towards the development of more sophisticated and robust methods for modeling and controlling complex systems. Recent research has focused on improving the accuracy and efficiency of existing methods, such as the use of artificial neural networks for state estimation and the development of novel optimization algorithms for control design. Another significant trend is the increasing use of data-driven approaches, which leverage large datasets to improve the performance of control systems. Furthermore, there is a growing interest in the application of control theory to emerging areas, such as neuromorphic control and human-robot interaction. Notable papers in this area include the proposal of a new approach to controlling linear dynamical systems, which achieves a running time that scales polylogarithmically with the inverse of the stability margin, and the introduction of a data-driven min-max model predictive control scheme for unknown discrete-time bilinear systems. Overall, the field is witnessing significant advancements in terms of methodology, application, and innovation, with a strong potential for impact in various fields, including engineering, robotics, and neuroscience. Noteworthy papers include: A New Approach to Controlling Linear Dynamical Systems, which proposes a novel method for controlling linear dynamical systems under adversarial disturbances and cost functions, and Bilinear Data-Driven Min-Max MPC, which introduces a data-driven min-max model predictive control scheme for unknown discrete-time bilinear systems.