学术报告第201515期

 报告题目：   Process Data Analytics Towards Bog  Data

 报 告 人：  S. Joe  Qin（Chinese University of Hong Kong）

 报告时间：  4月4日（星期六）下午14:30

 报告地点：  南一楼中311室

 邀 请 方：“多谱信息处理技术”国家级重点实验室

Abstract：

For engineering systems where  processes, units, and equipment are designed with well-specified purposes under  well-controlled operations, mechanistic models and principles are dependable.  However, for the operation of emerging or abnormal situations that are not  expected in the design, data become indispensable assets for the decision-making  in safe and efficient operations. In this talk we offer a perspective on the  essence of process data analytics, how data have been effectively used in  process operations and control, and new perspectives on how process systems  operations might evolve to a paradigm of data-enhanced operations and control.  The discussed perspectives include i) mining of time series data for event  discovery, decision-making, and causality analysis; ii) exploring the power of  new machine learning techniques that have enjoyed tremendous development in  nearly two decades; and iii) anticipating a system architecture shift towards a  data-friendly information system.

Speaker’s Short Bio:

Dr. S.  Joe Qin 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 Vice President of the Chinese University of Hong Kong, Shenzhen, and  is on leave from the position of Fluor Professor of Process Engineering at the  Viterbi School of Engineering of the University of Southern  California.

Dr.  Qin is a Fellow of IEEE and Fellow of the International Federation of Automatic  Control (IFAC). He is a recipient of the 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, Chang Jiang Professor of Tsinghua  University, Thousand Talent Professor of the Northeastern University of China,  and an IFAC Best Paper Prize for the model predictive control survey paper  published in Control  Engineering Practice. He is currently an Associate  Editor for Journal of  Process Control, IEEE Control Systems Magazine, and a  Member of the Editorial Board for Journal of  Chemometrics. He has published over 110 papers in SCI  journals, with over 5700 ISI Web of Science citations and an h-index of 39. Dr.  Qin’s research interests include process data analytics, process monitoring and  fault diagnosis, model predictive control, system identification, building  energy optimization, semiconductor process control, and control performance  monitoring.