简介
Dr. S. Joe Qin is the Wai Kee Kau Chair Professor of Data Science and President of Lingnan University, Hong Kong, China. He obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is currently Chair Professor, Dean of the School of Data Science, and Director of Hong Kong Institute for Data Science at City University of Hong Kong. In his prior career he worked as the Fluor Professor at the Viterbi School of Engineering of the University of Southern California, Professor at the University of Texas at Austin, and Principal Engineer at Emerson Process Management.
Dr. Qin is a member of European Academy of Sciences and Arts, Fellow of the Hong Kong Academy of Engineering, Fellow of the U.S. National Academy of Inventors, Fellow of the International Federation of Automatic Control (IFAC), Fellow of AIChE, and Fellow of IEEE. He is a recipient of the 2022 IEEE CSS Transition to Practice Award, the 2022 AIChE CAST Computing in Chemical Engineering Award, the U.S. National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, and recipient of the IFAC Best Paper Prize for a model predictive control paper published in Control Engineering Practice. He has served as Senior Editor of Journal of Process Control, Editor of Control Engineering Practice, Member of the Editorial Board for Journal of Chemometrics, and Associate Editor for several journals. He has published over 470 international journal papers, book chapters conference papers and presentations. He received over 38,000 Google Scholar citations with an h-index of 88. Dr. Qin’s research interests include data analytics, machine learning, process monitoring, model predictive control, system identification, smart manufacturing, smart cities, energy efficiency systems, and predictive maintenance.
