Abstract
Modern autonomous systems, like self-driving cars, unmanned aerial vehicles, or robots, are equipped with advanced sensing, learning, and perception modules. On one hand these modules render the overall system more informed, possibly providing predictions into the future. On the other hand, they can be unreliable, as in the case of vision-based perception algorithms unexpectedly failing to detect the obstacles. In this talk, I will discuss some of our recent work on problems that deal with synthesizing controllers to ensure safety and invariance in the presence of information imperfections or predictions. I will show problem instances in these different information regimes when control synthesis can be achieved in a scalable way. I will also discuss how these ideas can be extended to develop algorithms for corner case generation for testing and falsification purposes.
Brief Biography
Necmiye Ozay received the B.S. degree from Bogazici University, Istanbul in 2004, the M.S. degree from the Pennsylvania State University, University Park in 2006 and the Ph.D. degree from Northeastern University, Boston in 2010, all in electrical engineering. She was a postdoctoral scholar at the California Institute of Technology, Pasadena between 2010 and 2013. She joined the University of Michigan, Ann Arbor in 2013, where she is currently an associate professor of Electrical Engineering and Computer Science. She is also a core member of Michigan Robotics. Dr. Ozay’s research interests include hybrid dynamical systems, control, optimization and formal methods with applications in cyber-physical systems, system identification, verification & validation, autonomy and dynamic data analysis. Her papers received several awards including a Nonlinear analysis: Hybrid Systems Prize Paper Award for years 2014-2016. She has received the 1938E Award and a Henry Russel Award from the University of Michigan for her contributions to teaching and research, and five young investigator awards, including NSF CAREER.