Online Service

Online service research focuses on developing efficient and robust algorithms for real-time decision-making and learning in dynamic environments, addressing challenges like noisy data, concept drift, and limited resources. Current research emphasizes online learning frameworks, including gradient-based methods, dynamic mode decomposition, and graph neural networks, often incorporating techniques from control theory and reinforcement learning to improve performance and stability. These advancements have significant implications for various applications, such as personalized recommendations, autonomous navigation, and real-time control systems in domains ranging from robotics to power grids.