Cao, Yongzhi

Cao, Yongzhi


Research Interests: Formal methods, reasoning about uncertainty

Office Phone: 86-10-6276 5818


Cao, Yongzhi is a professor in the Department of Computer Science and Technology, School of EECS. He received the B.S. and M.S. degrees from Central China Normal University, Wuhan, China, in 1997 and 2000, respectively, and the Ph.D. degree from Beijing Normal University, Beijing, China, in 2003, all in mathematics. From 2003 to 2007, he was a Postdoctoral Researcher with Tsinghua University, Beijing, and from 2007 to 2105, he was an Associate Professor of computer science with Peking University. His current research interests include formal methods and reasoning about uncertainty in artificial intelligence.

Dr. Cao has published some papers in top-tier journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Computers, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Information and Computation, Journal of Computer and System Sciences, and Theoretical Computer Science. He has served in the Technical Program Committee of various international conferences including FMAC2017, ICCSIP2016, JRS2014, NIPS2015, and QLSC2012. He is serving as a reviewer of American Mathematical Reviews (MR) and a referee of some leading journals and conferences including AIJ, IEEE TAC, IEEE TASE, IEEE TKDE, IEEE TOC, CONCUR2016, NIPS2015, and IJCAI2016. He was awarded the 2nd prize of Natural Science Award of Chinese Education Ministry for outstanding achievements in scientific research (2015). He is an IEEE Senior Member.

Dr. Cao has almost ten research projects including NSFC and 973 programs. His research achievements are summarized as follows:

1)  He modeled the fault tolerance of mobile systems and discrete-event systems from a new perspective, quantitatively studied the reliability of the system behavior, and gave an approach to optimizing the system behavior. For the first time, this work combines the classical Shannon communication theory with the research of process algebra and introduces the idea of behavior similarity into the modeling and supervisory control of discrete-event systems.

2)  He proposed a formal framework to verify differential privacy in the context of probabilistic systems, gave a two-level logic, a privacy variant of the familiar Hennessy–Milner logic, to characterize differential privacy, and introduced the notion of set-theoretic conditional anonymity by considering the threat from non-probabilistic adversary.

3)  He modeled the discrete-event systems with fuzzy uncertainties, provided model-checking algorithms to verify the properties of the systems, and systematically developed the framework of supervisory control based on events and states. These theoretical results are applied to the modeling and analysis of medical diagnosis and treatment.