Real World System

Real-world system research focuses on designing, implementing, and verifying intelligent systems that operate effectively in complex, dynamic environments. Current efforts concentrate on developing self-adaptive architectures, leveraging AI (including large language models and deep learning) for tasks like task planning, tool usage, and data transformation, and employing digital twins for accurate simulation and optimization. This work is significant for improving the reliability and safety of critical systems across diverse domains, from wildfire detection to supply chain management, and for advancing the theoretical understanding of complex system behavior.