Sylvia Herbert

University of California, Berkeley

PhD Candidate

Sylvia Herbert is a PhD candidate in UC Berkeley’s Electrical Engineering and Computer Science Department working with Professor Claire Tomlin. She received her master’s and bachelor’s degree in mechanical engineering from Drexel University.
Her research interest is in developing practical and theoretically sound techniques for ensuring the safety of systems that must think and operate in real-time environments. Her research draws from cognitive science, optimal control theory, and machine learning. She has organized and led tutorials and workshops on this subject at major conferences (CDC and ICRA).
She has received the NSF Graduate Research Fellowship Award, the UC Berkeley Outstanding Graduate Student Instructor Award, and the UC Berkeley Chancellor’s Award. Outside of research she is active in service and mentorship, acting as President of both the UC Berkeley Electrical Engineering Graduate Student Association and the UC Berkeley EECS Peer Mentors Program.

Research Abstract:

My research aims to enable autonomous systems to operate safely and efficiently in real-world environments among humans and other agents. My work draws from control theory, cognitive science, and machine learning, and is backed by both rigorous theory and physical testing on robotic platforms. My key contributions are:
1) Forming new connections between cognitive science and scalable safe dynamic systems: adapting cognitive models of human decision-making to robot algorithms to mimic human strengths in real-time planning and to facilitate better human-robot interaction.
2) Advancing the theory of optimal control for dynamic games in high dimensions: introducing new formulations, theorems, and algorithms for scalable high-dimensional safety analysis.
3) Developing safe learning techniques for dynamic systems: developing online techniques for updating theoretical guarantees based on new information about system dynamics.