Data Science, Robotics and Controls
Campus Box 352500
University of Washington
Seattle, WA 98195
External Web Page: faculty.washington.edu/ratliffl
Lillian Ratliff obtained her PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2015. Prior to that Lillian obtained an MS in Electrical Engineering (2010) and BS degrees in Mathematics and Electrical Engineering (2008) all from the University of Nevada, Las Vegas. Her research interests lie at the intersection of learning, optimization, and game theory. She is the recipient of a National Science Foundation Graduate Research Fellowship (2009), a CISE Research Initiation Initiative Award (2017), and a CAREER Award (2019). She is also a recipient of the 2020 Office of Naval Research Young Investigator award, and was invited by the National Academy of Engineering to speak at the 2019 China-America Frontiers of Engineering Symposium.
Awards and Honors
- 2017 NSF CISE Research Initiation Initiative Award
- 2019 NSF CAREER
- 2019 Invited Speaker for NAE China-America Frontiers of Engineering Symposium
- 2020 Office of Naval Research Young Investigator award
- Tanner Fiez, Lalit Jain, Kevin Jamieson, and Lillian J. Ratliff. Sequential Experimental Design for Transductive Linear Bandits. Advances in Neural Information Processing Systems 32 (NeuRIPS), 2019.
- Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, and Samuel Burden. Convergence Guarantees for Gradient-Based Learning in Continuous Games. Uncertainty in Artificial Intelligence, 2019.
- Lillian J. Ratliff, Roy Dong, Shreyas Sekar, and Tanner Fiez. A Perspective on Incentive Design: Challenges and Opportunities. Annual Reviews of Controls, Robotics, and Autonomous Systems, 2019.
- Tanner Fiez, Shreyas Sekar, Liyuan Zheng, and Lillian J. Ratliff. Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences. Uncertainty in Artificial Intelligence, 2018.
- Eric Mazumdar, Lillian J. Ratliff, Shankar Sastry, Michael I. Jordan. Policy Gradient in Linear Quadratic Dynamic Games Has No Convergence Guarantees. Smooth Games Optimization and Machine Learning Workshop: Bridging Game Theory and Deep Learning, NeuRIPS, 2019.
- Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff. Convergence of Learning Dynamics in Stackelberg Games. Smooth Games Optimization and Machine Learning Workshop: Bridging Game Theory and Deep Learning, NeuRIPS, 2019.
- Benjamin Chasnov, Tanner Fiez, Lillian J. Ratliff. Opponent Anticipation via Conjectural Variations. Smooth Games Optimization and Machine Learning Workshop: Bridging Game Theory and Deep Learning, NeuRIPS, 2019.
- Tanner Fiez
- Benjamin Chasnov
- Liyuan Zheng
- Mitas Ray