Invited Keynote Speakers

Since co-founding the company in January 2000, Robin has grown Baidu into the largest Chinese search engine. Baidu now has over 75% market share in search in China, and ranks as the third largest independent search engine in the world. In 2005, Baidu completed a successful IPO on NASDAQ, and in 2007 became the first Chinese company to be included in the NASDAQ-100 Index. Today, Baidu is among the most valuable brands in China. The Financial Times named Baidu one of the “Top 10 Chinese Global Brands,” making it the youngest and only Internet company on the list.

Robin Li was named “China Economic Figure of the Year 2005” by CCTV and one of “China’s Most Powerful Entrepreneurial Thought Leaders” by China Entrepreneurs Forum. He was also named one of the “Best Business Leaders in the World” by Business Week Magazine and one of the “Most Influential Chinese Business Leaders” by Fortune Magazine. In 2010, Robin Li was named by both Forbes and Times magazine as one of the “Most Influential People in the World.”

Robin received a Bachelor of Science Degree in Information Management from Peking University in 1991, and a Master of Science Degree in Computer Science from the State University of New York at Buffalo in 1994.

He served as the founding Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (TKDD) and on the editorial boards of several other journals. Jiawei has received ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), and IEEE Computer Society W. Wallace McDowell Award (2009), and Daniel C. Drucker Eminent Faculty Award (2011). He is a Fellow of ACM and IEEE. He is currently the Director of Information Network Academic Research Center (INARC) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab. His book with Micheline Kamber and Jian Pei, "Data Mining: Concepts and Techniques" (Morgan Kaufmann) has been used worldwide as a textbook.

His research in recent years has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, variational methods, kernel machines and applications to problems in statistical genetics, signal processing, computational biology, information retrieval and natural language processing.

Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He is a Fellow of the ACM, the CSS, the IMS, the IEEE, the AAAI and the ASA.

His research interests include topics in machine learning, algorithmic game theory, social networks, computational finance, and artificial intelligence. He often examines problems in these areas using methods and models from theoretical computer science and related disciplines. While the majority of his work is mathematical in nature, he has also participated in a variety of empirical and experimental projects, including applications of machine learning to finance, spoken dialogue systems, and other areas. Most recently, he has been conducting human-subject experiments on strategic and economic interaction in social networks.