Michael Lemmon

Prof. Michael Lemmon

Dept. of Electrical Engineering
University of Notre Dame
Fitzpatrick Hall 266
(Michael.D.Lemmon.1 AT nd. edu)
574-631-8309

Biography: Michael Lemmon was born in Tacoma Washington and raised in California. He did his undergraduate work at Stanford University with an emphasis on mathematical control theory. After graduating from Stanford in 1979, he worked for seven years as an aerospace engineer in companies such as TRW, Lockheed,and General Electric. He returned to graduate school in 1986 and obtained the M.S. and Ph.D. degrees in electrical engineering from Carnegie-Mellon University in 1990. His doctoral work focused on the use of neural networks in statistical signal processing. He joined the faculty of electrical engineering at the University of Notre Dame in 1990. He worked for many years with networked control systems. He is probably best known for his work on event-triggered control systems as well as having started a project that deployed one of the first municipal scale sensor-actuator networks for wastewater management in smart-cities. His current work is exploring deep learning for adaptive control.

Teaching: 2021-2022 Courses:

Fall 2024 - EE 60550-1 - Linear Systems Theory TR 9:30 - 10:45 am - Debartolo 231
Spring 2025 - EE 60655-1 - Advanced Control - MW 2pm-3:15pm - Coleman Morse Ctr 231
Spring 2025 - EE 60572-1 - Introduction to Deep Learning - TTh 2pm-3:15pm - DBRT 303

Research: (google scholar) Many large-scale systems are automatically managed through networks of computers that are tied to sensors and actuators. The resulting sensor-actuator networks (also called networked control systems) fuse computational and physical processes in a manner that is often poorly understood. Dr. Lemmon's current research seeks to understand the inter-relationship between communication, computation, and control in such large-scale sensor-actuator networks. His recent work has been studying the use of an "event-triggered" approach to control, estimation, and optimization in which agents in a sensor-actuator network transmit information to their neighbors when some internal measure of information novelty exceeds a specified threshold. Event-triggered communication and feedback appear to provide a way of dramatically reducing communication network usage while maintaining a specified level of overall networked system performance. Dr. Lemmon's research seeks to better understand the benefits and limitations of such networked control systems from both a theoretical and practical perspective. His theoretical work explores the fundamental principles that limited communication and computational resources have on a networked system's performance. Dr. Lemmon's work has also applied these ideas to real-life sensor-actuator networks used in managing important components of the nation's civil infrastructure such as municipal wastewater systems and the national power grid. His recent work is turning to examine the role of deep learning in adaptive control of complex dynamical systems that are open to the environment.