Published: Sept. 14, 2021

Morteza LahijanianMorteza Lahijanian
Assistant Professor, Smead Aerospace
Monday, Sept. 20 | 12:00 P.M. | Zoom Webinar

Abstract: Autonomous systems are poised to become an integral part of our economy, infrastructure, and society. They are rapidly gaining more capabilities via AI components and serving in ever more safety-critical roles. However, as their complexities grow, the more impossible it becomes to provide safety guarantees for their decisions and overall performance. In this talk, I argue that formal methods combined with control theory and machine learning techniques can form powerful tools to address this problem.

To this end, I present our recent contributions that focus on data-driven approaches to formal analysis and control of highly uncertain systems.  Specifically, I focus on a framework for correct-by-construction control synthesis based on Gaussian regression, finite abstraction, and automata learning. I also discuss the importance of explanations in autonomous decision making and present our recently developed explanation scheme that provides easily-interpretable and verifiable explanations in the context of multi-agent planning.

Bio: Morteza Lahijanian is an assistant professor in the Aerospace Engineering Sciences department, an affiliated faculty at the Computer Science department, and the director of the Assured, Robust, and Interactive Autonomous (ARIA) Systems group at the University of Colorado Boulder. He received a B.S. in Bioengineering at the University of California, Berkeley in 2005, an M.S. in Mechanical Engineering at Boston University in 2009, and a Ph.D. in the field of systems and control theory with applications in robotics at Boston University in 2013. He served as a postdoctoral scholar in Computer Science at Rice University from 2013 to 2015.

Prior to joining CU Boulder in 2018, he was a research scientist in the department of Computer Science at the University of Oxford. His awards include Ella Mae Lawrence R. Quarles Physical Science Achievement Award, Jack White Engineering Physics Award, NSF Fellowship, and Wadham College Research Fellowship. Dr. Lahijanian’s research interests span the areas of control theory, stochastic hybrid systems, formal methods, and game theory with applications in robotics, particularly, motion planning, strategy synthesis, model checking, and human-robot interaction. His lab develops novel theoretical foundations and computational frameworks to enable reliable and intelligent autonomy. The emphasis is especially on safe autonomy through correct-by-construction algorithmic approaches.