Qi Zhu

Qi Zhu is a Professor of Electrical and Computer Engineering and (by courtesy) Computer Science at Northwestern University. His research interests include algorithm, software, and hardware layers for building safe, secure, and robust foundation model-driven AI systems, with efforts in formal safety verification and robustness certification, safety and security analysis of foundation model-driven embodied agents, safety-assured learning and system design, multimodal time-series learning, continual learning, and federated learning. His work also centers on design automation for cyber-physical systems (CPS), cyber-physical security, energy-efficient CPS, and system-on-chip design.

Qi Zhu is a Professor of Computer Engineering at Northwestern University, specializing in Design Automation, Cyber-Physical Systems, Safe and Robust ML, and Embodied AI. His research focuses on developing methodologies for intelligent control systems, particularly in building HVAC, automotive systems, and distributed real-time environments. He explores techniques such as deep reinforcement learning, federated learning, and reachability analysis for neural networks to ensure safety and robustness in complex systems. His work also addresses challenges in task allocation, priority assignment, and secure communication in cyber-physical systems. Zhu's contributions span both theoretical advancements and practical implementations in embedded and real-time systems.