The field of electronic-photonic design automation and AI-driven optimization is rapidly advancing, with a focus on developing innovative solutions to address the complexity and scalability challenges of modern electronic and photonic systems. A common theme among recent research efforts is the integration of machine learning and optimization techniques to improve the performance, efficiency, and reliability of these systems. Notable developments include the application of curvy waveguides, bending, and port alignment in photonic integrated circuit design, as well as the use of computing-in-memory architectures and neural architecture search frameworks to optimize machine learning workloads. Recent papers have demonstrated significant improvements in performance, energy efficiency, and design cycle time through the use of AI-driven optimization techniques in areas such as antenna miniaturization, RF/analog circuit design, and approximate computing. The introduction of novel frameworks and algorithms, such as LiDAR 2.0, CIM-NET, FALCON, and CrossNAS, has further advanced the field. In addition to electronic-photonic design automation, researchers are also exploring new techniques to improve the performance of optimization algorithms, including the development of near-optimal algorithms and solver-free training methods. The use of uncertainty sets and robust constraints is becoming increasingly important in stochastic linear optimization, while dynamic regret guarantees and optimistic composition of future costs are key areas of research in online convex optimization. The field of decision-making under uncertainty is also rapidly advancing, with a focus on developing innovative methods to improve the accuracy and efficiency of decision-making processes. Researchers are exploring new approaches to address complex problems, such as multi-objective optimization, bandit optimization, and fair division, with significant implications for real-world applications. Furthermore, the field of optimization algorithms and meta-learning is witnessing significant developments, with a focus on developing more efficient and effective methods for solving complex problems. The creation of frameworks that can generate effective optimizers for specific problems, as well as the introduction of new algorithms that outperform existing methods, is driving innovation in this area. Lastly, the field of Bayesian optimization and uncertainty quantification is improving the efficiency and accuracy of optimization algorithms through the exploration of new probabilistic models and interactive hyperparameter optimization. Overall, these advancements demonstrate the rapid progress being made in electronic-photonic design automation and AI-driven optimization, with significant implications for a wide range of applications and fields.