Prof. Michael Irwin Jordan was named as Distinguished Visiting Professor of Tsinghua Univeristy by its Academic review board. In the tenure as a Distinguished Visiting Professor at Tsinghua University, Professor Jordan will promote the substantive cooperation with Tsinghua University on regular visits, participating research projects, jointly supervising Ph.D students, etc. and especially he will work closely with Big Data research team of School of Software, Tsinghua University on the research areas of machine learning, data mining, artificial intelligence and distributed algorithm. Michael Irwin Jordan is an American scientist, Professor at the University of California, Berkeley and leading researcher in machine learning and artificial intelligence. Jordan received his BS magna cum laude in Psychology in 1978 from the Louisiana State University, his MS in Mathematics in 1980 from Arizona State University and his PhD in Cognitive Science in 1985 from the University of California, San Diego. At the University of California, San Diego Jordan was a student of David Rumelhart and a member of the PDP Group in the 1980s. Jordan is currently a full professor at the University of California, Berkeley where his appointment is split across the Department of Statistics and the Department of EECS. He was a professor at MIT from 1988-1998. In the 1980s Jordan started developing recurrent neural networks as a cognitive model. In recent years, though, his work is less driven from a cognitive perspective and more from the background of traditional statistics. He popularised Bayesian networks in the machine learning community and is known for pointing out links between machine learning and statistics. Jordan was also prominent in the formalisation of variational methods for approximate inference and the popularisation of the expectation-maximization algorithm in machine learning. Jordan received numerous awards, including a best student paper award (with X. Nguyen and M. Wainwright) at the International Conference on Machine Learning (ICML 2004), a best paper award (with R. Jacobs) at the American Control Conference (ACC 1991), the ACM - AAAI Allen Newell Award, the IEEE Neural Networks Pioneer Award, and an NSF Presidential Young Investigator Award. In 2010 he was named a Fellow of the Association for Computing Machinery "for contributions to the theory and application of machine learning." Prof. Jordan is a member of the National Academy of Science, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences.