Our research explores the structure, dynamics, and function of complex systems, bridging network science, artificial intelligence, and control theory. We develop theoretical and computational frameworks to uncover universal resilience patterns across domains such as transportation, power grids, biology, healthcare, and social systems. We also pursue diverse yet interconnected directions, including neural architecture search and model selection through network-theoretic approaches, agentic AI and in-context learning, quantum communication networks, and the dynamics of networks with incomplete or uncertain information. Together, these efforts aim to build a unified understanding of how intelligent, resilient, and adaptive behaviors emerge across natural and engineered networks.