The field of control systems and neural networks is rapidly evolving, with a focus on developing more efficient and robust models. Recent research has explored the use of graph neural networks (GNNs) for active flow control, which has shown significant potential in learning complex control strategies. Additionally, novel methods for training recurrent neural networks (RNNs) have been proposed, which can reduce the computational costs associated with traditional training methods. Furthermore, researchers have made progress in developing predictive models for terrain manipulation and robotic plaster printing, which can be used to improve the efficiency and accuracy of these processes. The development of new parameterization techniques for state-space models has also been a major area of focus, with applications in system identification and optimal control. Notable papers include: Invariant Control Strategies for Active Flow Control using Graph Neural Networks, which demonstrates the effectiveness of GNNs in active flow control. Fast Training of Recurrent Neural Networks with Stationary State Feedbacks, which proposes a novel method for training RNNs using a fixed gradient feedback mechanism. Localized Graph-Based Neural Dynamics Models for Terrain Manipulation, which introduces a learning-based approach for terrain dynamics modeling and manipulation. Free Parametrization of L2-bounded State Space Models, which proposes a novel parametrization of state-space models that guarantees input-output stability and robustness. Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing, which proposes a GNN modeling approach to predict the resulting surface from a particle-based fabrication process. R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks, which presents a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control.