Michael Lemmon is a Professor in the Department of Electrical Engineering at the University of Notre Dame's College of Engineering. He has been a faculty member at Notre Dame since 1990, contributing significantly to the field of networked control systems and related applications. Education: Ph.D., Electrical Engineering, Carnegie Mellon University, 1990 M.S., Electrical Engineering, Carnegie Mellon University, 1990 B.S., Mathematics, Stanford University, 1979 Professor Lemmon's research focuses on understanding the interrelationship among communication, computation, and control in large-scale sensor-actuator networks. He is particularly known for his pioneering work on event-triggered control systems and for deploying one of the first municipal scale sensor-actuator networks for wastewater management. His current research explores deep learning applications for adaptive control of complex dynamical systems. His work spans theoretical foundations of networked control systems to practical applications in critical infrastructure including smart grids, power systems, and water management systems. Analysis of Professor Lemmon's recent publications reveals a strong focus on event-triggered and self-triggered control methodologies for networked systems. His work bridges theoretical control theory with practical applications in power systems and sensor networks. Key themes include communication efficiency in control systems, stability analysis of networked systems, and the application of these principles to real-world infrastructure challenges. Current Research Projects: "Using Data Science to Protect Tap Water Quality" (Lucy Family Institute, 2022-2023) - Using data science to identify homes at risk for unhealthy tap water and develop mitigation strategies "CPS: SMALL: Learning How to Control - A Meta-Learning Approach for the Adaptive Control of Cyber-Physical Systems" (NSF, 2023-2026) - Developing machine learning algorithms for adaptive control of IoT-enabled manufacturing systems Professor Lemmon teaches several courses including Systems Theory and Applications (EE 30122), Advanced Control (EE 60655), and Introduction to Deep Learning (EE 60572). His teaching spans both undergraduate and graduate levels, with a focus on control systems theory and emerging applications of machine learning in control engineering.









