Sam Coogan

Job title: 
Associate Professor
Department: 
Alumni
Georgia Institute of Technology
Bio/CV: 

Dissertation: Synthesis and Verification of Networked Systems with Applications to Transportation Networks

Advisors: Murat Arcak, Pravin Varaiya

PhD Electrical Engineering, University of California Berkeley, 2015

MS Electrical Engineering, University of California Berkeley, 2012

BS Electrical Engineering, Georgia Institute of Technology, 2010

Georgia Institute of Technology - Present 

  • Assistant Professor
  • Demetrius T. Paris Junior Professor

Sam Coogan received the B.S. degree in Electrical Engineering from Georgia Tech and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley. In 2015, he was a postdoctoral research engineer at Sensys Networks, Inc., and in 2012 he was a research intern at NASA's Jet Propulsion Lab. Before joining Georgia Tech in 2017, he was an assistant professor in the Electrical Engineering department at UCLA from 2015–2017.

At UC Berkeley, Coogan received the 2016 Eli Jury Award for outstanding achievement in the area of control and the 2014 Leon O. Chua Award for outstanding achievement in nonlinear science. He received the best student paper award at the 2015 Hybrid Systems: Computation and Control conference, the outstanding paper award for the IEEE Transactions on Control of Network Systems in 2017, a CAREER award from the National Science Foundation in 2018, a Young Investigator Award from the Air Force Office of Scientific Research in 2018, and the Donald P Eckman Award from the American Automatic Control Council in 2020.

Research interests: 

My research is in the area of dynamical systems and autonomy and focuses on developing scalable tools for verification and control of networked, cyber-physical systems. I am especially interested in applying these tools to create efficient, intelligent, and autonomous transportation systems. My research contributes to and draws from domains including control theory, nonlinear and hybrid systems theory, formal methods, learning in probabilistic systems, and optimization.