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Paper 15: On the Robustness of Safe Reinforcement Learning under Observational Perturbations
Zuxin Liu · Zhepeng Cen · Huan Zhang · Jie Tan · Bo Li · Ding Zhao

Author Information

Zuxin Liu (Carnegie Mellon University)
Zhepeng Cen (Carnegie Mellon University)
Huan Zhang (CMU)
Jie Tan (Google Inc.)
Bo Li (UIUC)
Bo Li

Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, AI’s 10 to Watch, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, IBM, and eBay, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Ding Zhao (Carnegie Mellon University)

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