The field of optimization and machine learning is moving towards more scalable and efficient methods. Researchers are developing new algorithms and frameworks that can handle complex problems and large datasets. One of the key directions is the use of differentiable optimization, which allows for the integration of optimization and machine learning techniques. This has led to the development of new tools and frameworks that can be used for a wide range of applications, from contract design to reinforcement learning. Another area of focus is the use of mean-field games and parameterized approximations to design incentives for multi-agent systems. These methods have been shown to be effective in a variety of settings, including auction design and reinforcement learning. Noteworthy papers include: Scalable Principal-Agent Contract Design via Gradient-Based Optimization, which introduces a generic algorithmic framework for contract design using modern machine learning techniques. Scalable Neural Incentive Design with Parameterized Mean-Field Approximation, which formalizes the incentive design problem as a parameterized mean-field game and introduces a novel algorithm for computing gradients efficiently. Enhancing Hierarchical Reinforcement Learning through Change Point Detection in Time Series, which integrates a self-supervised change point detection module into the Option-Critic framework to enable adaptive segmentation of state trajectories and discovery of options.