Xenofon Koutsoukos
Chair, Department of Computer Science
Professor of Computer Science
Professor of Electrical and Computer Engineering
Xenofon Koutsoukos is a professor with the Department of Electrical Engineering and Computer Science and a senior research scientist with the Institute for Software-Integrated Systems, Vanderbilt University. Before joining Vanderbilt, Dr. Koutsoukos was a Member of Research Staff in the Xerox Palo Alto Research Center (PARC) (2000-2002), working in the Embedded Collaborative Computing Area. He received his PhD in Electrical Engineering from the University of Notre Dame in 2000. His research work is in the area of cyber-physical systems with emphasis on formal methods, distributed algorithms, diagnosis and fault tolerance, and adaptive resource management. He has published numerous journal and conference papers and he is co-inventor of four US patents. He was the recipient of the NSF Career Award in 2004, the Excellence in Teaching Award in 2009 from the Vanderbilt University School of Engineering, and the 2011 NASA Aeronautics Research Mission Directorate (ARMD) Associate Administrator (AA) Award in Technology and Innovation. He is a Fellow of IEEE.
Education
Ph.D., Electrical Engineering
University of Notre Dame
M.S., Electrical Engineering
University of Notre Dame
M.S., Applied Mathematics
University of Notre Dame
Diploma, Electrical and Computer Engineering
National Technical University of Athens
Projects
- Science of Security Lablet
- CPS: Small: Integrated Reconfigurable Control and Moving Target Defense for Secure Cyber-Physical Systems
- Pre-curser for Fully Distributed Control of Powergrids
- Lablet Education and Outreach Activities: Cyber Makerspace, Vanderbilt Digital Nights, CPS Summer Camps
- Science of Security Virtual Organization
- CPS: Small: Integrated Reconfigurable Control and Moving Target Defense for Secure Cyber-Physical Systems: REU Supplemental Funding Request
- Neurosymbolic Autonomous Agents for Cyber-defense
- Collaborative Research: Network Control Systems Science for Graph Machine Learning
- Resilient Fully Distributed Learning