Assistant Professor of Computer Science and Electrical and Computer Engineering
Education
- 2022, Doctoral Degree, Electrical Engineering, Stanford University
- 2019, Master's Degree, Electrical Engineering, Stanford University
- 2017, Bachelor's Degree, Electrical and Electronics Engineering, Bilkent University
Biography
Erdem Bıyık is an assistant professor in Thomas Lord Department of Computer Science at the University of Southern California, and in Ming Hsieh Department of Electrical and Computer Engineering by courtesy. He leads the Learning and Interactive Robot Autonomy Lab (Lira Lab). Prior to joining USC, he was a postdoctoral researcher at UC Berkeley's Center for Human-Compatible Artificial Intelligence. He received his Ph.D. and M.Sc. degrees in Electrical Engineering from Stanford University, working at the Stanford Artificial Intelligence Lab (SAIL), and his B.Sc. degree in Electrical and Electronics Engineering from Bilkent University in Ankara, TĆ¼rkiye. During his studies, he worked at the research departments of Google and Aselsan. Erdem was an HRI 2022 Pioneer and received an honorable mention award for his work at HRI 2020. His works were published at premier robotics and artificial intelligence journals and conferences, such as IJRR, CoRL, RSS, NeurIPS.
Research Summary
Erdem Bıyık is broadly interested in artificial intelligence for robotics. In his research, he uses tools from machine learning, artificial intelligence, optimization, game theory, robotics, information theory, and cognitive science. Specifically, he works on robot learning from humans, human-robot collaboration, and learning in multi-agent systems.
In Lira Lab, Erdem's research group develops algorithms for robot learning, safe and efficient human-robot interaction and multi-agent systems. Their mission is to equip robots, or more generally agents powered with artificial intelligence (AI), with the capabilities that will enable them to intelligently learn, align with, adapt to, and influence the humans and other AI agents. They take a two-step approach to this problem. First, machine learning techniques that they develop enable robots to model the behaviors and goals of the other agents by leveraging different forms of information they leak or explicitly provide. Second, these robots interact with the others to achieve online adaptation by leveraging the learned behaviors and goals while making sure this adaptation is beneficial and sustainable.
