报告时间（Time）：2020 - 4 - 24（周五）10 am - 11am
报告通过ZOOM网上举行，请您通过金数据报名，我们稍后把会议链接发给您邮箱（Please register via the following link and you shall receive an email for the link to join the seminar.）
Trustworthy Autonomy: Robust Decision-Making and Adaptive Validation
Dr. Katie Driggs-Campbell (https://krdc.web.illinois.edu/ )
Assistant Professor, Electrical and Computer Engineering Department, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign (UIUC)
Affiliate Appointments: Computer Science Department, Center for Digital Agriculture
Autonomous systems, such as self-driving cars, are becoming tangible technologies that will soon impact the human experience. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can handle the unique challenges humans present: People tend to defy expected behaviors and do not conform to many of the standard assumptions made in robotics. To design safe, trustworthy autonomy, we must transform how intelligent systems interact, influence, and predict human agents. My work focuses on human-centered autonomy, using tools from robotics, artificial intelligence, and control to build intelligent systems that safely interact with people. In this talk, I'll present recent efforts on developing robust learning systems and on validating stochastic systems. We’ll show how these algorithms can be used to improve decision-making and control of a fully equipped test vehicle to operate safely on the road.
Katie Driggs-Campbell is currently an assistant professor at the University of Illinois at Urbana-Champaign in the Department of Electrical and Computer Engineering. Prior to that, she was a Postdoctoral Research Scholar at the Stanford Intelligent Systems Laboratory in the Aeronautics and Astronautics Department. She received a B.S.E. with honors from Arizona State University in 2012 and an M.S. from UC Berkeley in 2015. She earned her PhD in 2017 in Electrical Engineering and Computer Sciences from the University of California, Berkeley. Her lab works on human-centered autonomy, focusing on the integration of autonomy into human dominated fields, merging ideas robotics, learning, human factors, and control.