Author IDs

Willing to take PhD students: yes


Society Fellows

  • Fellow, U.S. Academy of Inventors
  • Fellow, American Institute of Chemical Engineers (AIChE)
  • Fellow, Institute of Electrical and Electronics Engineers (IEEE), and
  • Fellow, International Federation of Automatic Control (IFAC).


  • Doctoral Degree, Chemical Engineering, University of Maryland College Park, USA
  • Master's Degree, Automatic Control, Tsinghua University, Beijing, China
  • Bachelor's Degree, Automation, Tsinghua University, Beijing, China


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 began his professional career in 1992 as a principal engineer at Emerson Process Management, a subsidiary of Emerson Electric, to work on advanced process control. After having developed two advanced control products, he joined the University of Texas at Austin as an assistant professor in 1995. He was promoted to associate professor and professor in 2000 and 2003, respectively, and was the holder of the Paul D. and Betty Robertson Meek and American Petrofina Foundation Centennial Professorship in Chemical Engineering until 2007. From 2007 to 2019 he was the Fluor Professor at the Viterbi School of Engineering of the University of Southern California. He was co-director the Texas-Wisconsin-California Control Consortium (TWCCC) where he was Co-PI for 24 years to conduct research on industry-sponsored projects. His research has directly impacted around 50 corporations who have been members of the Consortium. He is currently Chair Professor of Data Science at the City University of Hong Kong.


Dr. Qin’s research interests include data analytics, machine learning, latent variable methods; high-dimensional time series latent variable modeling, process monitoring and fault diagnosis, model predictive control, system identification, semiconductor manufacturing control, and data-driven control and optimization. He has over 400 publications in international journals, book chapters, conference papers, and conference presentations with peer-reviewed abstracts. He delivered over 50 invited plenary or keynote speeches and over 120 invited technical seminars worldwide.


He is a recipient of CAST Computing in Chemical Engineering Award by the American Institute for Chemical Engineers, 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, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published in Control Engineering Practice. He 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 other journals.

Research Interests/Areas

Industrial Data Analytics, Statistical Machine Learning, Data Science for Systems, Latent Variable Methods; High-dimensional Time Series Modeling, Data-driven Control and Optimization, Energy and Process Systems

Selected Publications

[Summary on Citations]
  • Web of Science citations: 18,000; h-index: 64
  • Scopus citations: 22,858; h-index of 71
  • Google Scholar citations: 35,700; h-index of 81

[Summary of Publications and Presentations]

  • Archival Journal Papers/ Book Chapters 【Total: 170】
  • Archival Conference Papers 【Total: 146】
  • U.S. Patents or U.S. Patent-pending【17】
  • Plenary/Keynote/Invited Presentations 【Total: 63】
  • Invited Seminar Presentations 【Total: 123】
  • Presentations with Refereed Abstracts 【Total: 70】
  • Short Courses and Workshops 【24】

 [Selected Journal Papers]

  1. Yu, J., & Qin, S. J. (2022). Latent State Space Modeling of High-Dimensional Time Series with a Canonical Correlation Objective. IEEE Control Systems Letters, 6, 3469-3474.
  2. Qin, S. Joe (2022). Latent Vector Autoregressive Modeling and Feature Analysis of High Dimensional and Noisy Data from Dynamic Systems, AIChE Journal, 68(6): e17703. doi:10.1002/aic.17703
  3. Qiang Liu, Xuecheng Ding and S. Joe Qin (2022). Reconstruction and Magnitude Estimation for Fault Diagnosis of Dynamic Processes with an Industrial Application, Control Engineering Practice, April 2022, 105008.
  4. Fan, Jicong, Tommy W.S. Chow, and Qin, S. Joe (2022). Kernel Based Statistical Process Monitoring and Fault Detection in the Presence of Missing Data, IEEE Transactions on Industrial Informatics, 18(7), 4477–4487.
  5. Zhan, Q. Liu, C. Wang, and S. Joe Qin (2021). Towards Lightweight Dynamic Convolutional Neural Network Modeling for Soft Sensors, submitted for publication to IEEE Transactions on Cybernetics, No. CYB-E-2021-12-3396, Dec. 2021
  6. Qin, S. Joe and Yiren Liu (2021). A Stable Lasso Algorithm for Inferential Sensor Structure Learning and Parameter Estimation”, Journal of Process Control, 107, November 2021, Pages 70-82.
  7. Joe Qin, Siyi Guo, Zheyu Li, Leo H. Chiang, Ivan Castillo, Birgit Braun, Zhenyu Wang (2021). Integration of Process Knowledge and Statistical Learning for the Dow Data Challenge Problem, Computers and Chemical Engineering. 153, 107451, October 2021
  8. Dong, Yining, Qin, S. Joe, and Stephen Boyd (2021). Extracting a Low-Dimensional Predictable Time Series", Optimization and Engineering, published in May 2021
  9. Qin, S. Joe, Y. Liu, and Dong, Yining (2021). Plant-Wide Troubleshooting and Diagnosis Using Dynamic Embedded Latent Feature Analysis. Computers and Chemical Engineering. 152, 107392, published in May 2021
  10. Joe Qin, Yining Dong, Qinqin Zhu, Jin Wang, and Qiang Liu (2020). Bridging Systems Theory and Data Science: A Unifying Review of Dynamic Latent Variable Analytics and Process Monitoring, Annual Reviews in Control, 50, 29-48
  11. Yuan Jin, S. Joe Qin, and Qiang Huang (2020). Modeling Inter-layer Interactions for Out-of-Plane Shape Deviation Reduction in Additive Manufacturing. IISE Transactions, 52:7, 721-731
  12. Qinqin Zhu, S. Joe Qin, and Yining Dong (2020). Dynamic Latent Variable Regression for Inferential Sensor Modeling, Computers and Chemical Engineering, 137: 106809.
  13. Dong, Yining and Qin, S. Joe (2020). New Dynamic Predictive Monitoring Schemes based on Dynamic Latent Variable Models, I&EC Research, 59 (6), 2353-2365.
  14. Dong, Yining, Y. Liu, and Qin, S. Joe (2020). Efficient Dynamic Latent Variable Analysis for High Dimensional Time Series Data, IEEE Transactions on Industrial Informatics. 16(6), 4068-4076.
  15. Ruonan Liu, F. Wang, Boyuan Yang and S.J. Qin (2020). Multi-scale Kernel based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions, IEEE Transactions on Industrial Informatics. 16(6), 3797 – 3806.
  16. S. Joe Qin and Leo H. Chiang (2019). Advances and Opportunities in Machine Learning for Process Data Analytics. Computers and Chemical Engineering, 126, Pages 465 - 473.
  17. Qinqin Zhu, S. Joe Qin (2019). Supervised Diagnosis of Quality and Process Faults with Canonical Correlation Analysis. I&EC Research, Special Issue for Sirish Shah Festschrift, 58(26), 11213-11223.
  18. Dong, Yining, and S. Joe Qin (2018). Regression on dynamic PLS structures for supervised learning of dynamic data. Journal of Process Control, 68, 64-72.
  19. Dong, Yining, and S. Joe Qin (2018). A Novel Dynamic PCA Algorithm for Dynamic Data Modeling and Process Monitoring. Journal of Process Control, 67, Pages 1-11. 
  20. Dong, Yining, and S. Joe Qin (2018). Dynamic Latent Variable Analytics for Process Operations and Control. Computers and Chemical Engineering, 114, Pages 69-80.
  21. Qinqin Zhu, Qiang Liu, S. Joe Qin (2017). Concurrent Quality and Process Monitoring with Canonical Correlation Analysis. Journal of Process Control, 60, 95-103.
  22. Qiang Liu, S. Joe Qin, Tianyou Chai (2017). Unevenly Sampled Data Modeling and Monitoring of Dynamic Processes with an Industrial Application, IEEE Transactions on Industrial Informatics, 13: 2203 - 2213.
  23. S.J. Qin (2014). Process Data Analytics in the Era of Big Data, Perspective paper, AIChE Journal, 60, 3092-3100.
  24. Jingran Ma, S. Joe Qin, and Tim Salsbury (2014). Application of Economic MPC to the Energy and Demand Minimization of a Commercial Building, Journal of Process Control, 24, 1282-1291.
  25. Gang Li, S. Joe Qin, and Donghua Zhou (2014). A New Method of Dynamic Latent Variable Modeling for Process Monitoring, IEEE Transactions on Industrial Electronics, 61, 6438 - 6445.
  26. Tao Yuan and S.J. Qin (2014). Root Cause Diagnosis of Plant-wide Oscillations Using Granger Causality, Journal of Process Control, 24, Pages 450-459.
  27. S. Joe Qin and Y.Y. Zheng (2013). Quality-relevant and Process-relevant Fault Monitoring with Concurrent Projection to Latent Structures. AIChE Journal, 59, 496-504.
  28. Qiang Liu, Tianyou Chai, H. Wang, and S. Joe Qin (2011). Data-Driven and Model-Driven Tension Estimation and Fault Diagnosis of Cold Rolling Continuous Annealing Processes. IEEE Trans. on Neural Networks, 22(12), 2284-2295.
  29. Gang Li, B. Liu, S. Joe Qin, and Donghua Zhou (2011). Quality relevant data-driven modeling and monitoring of multivariate dynamic processes: Dynamic T-PLS approach. IEEE Trans. on Neural Networks, 22(12), 2262-2271.
  30. S.J. Qin, Gregory Cherry, Richard Good, Jin Wang, and Christopher A. Harrison (2006). Semiconductor Manufacturing Process Control and Monitoring: A Fab-wide Framework. J. of Process Control, 16, 179-191.
  31. S.J. Qin and T.A. Badgwell (2003).  A survey of industrial model predictive control technology, Control Engineering Practice, 11(7), 733-764.
  32. S.J. Qin (2003).  Statistical process monitoring: basics and beyond, J. Chemometrics, 17, 480-502.
  33. Li, W., H. Yue, S. Valle-Cervantes, and Qin, S.J. (2000).  Recursive PCA for adaptive process monitoring, J. of Process Control, 10, 471 - 486.
  34. R. Dunia and Qin, S.J. (1998).  A subspace approach to multidimensional fault identification and reconstruction, AIChE Journal, 44(8), 1813-1831.



  • CAST Computing in Chemical Engineering Award, 2022, by American Institute of Chemical Engineers (AIChE).
  • Grand Prize: Microsoft Outstanding AI Influencer Award (Academic Group) and Gold Award, Global AI Challenge for Building E&M Facilities organized by EMSD of Hong Kong, 2022. Project Supervisors: S Joe Qin and XY Zhao; Team leader: Yiren Liu (SDSC PhD student). Team members: SL Yao, YX Huang, and G Han (undergraduate EE students).
  • World’s Top 2% Most-cited Scientists published by Stanford University, 2020, 2021.
  • The IAI Conference Best Paper Award, Yining Dong, Yingxiang Liu, S. Joe Qin, Dynamically Embedded Latent Feature Analysis for Plant-Wide Troubleshooting, the 3rd International Conference on Industrial Artificial Intelligence, Nov. 8-11, 2021, Shenyang, China.
  • The IEEE INFOCOM 2021 Best Poster Award. Yanfang Mo, Qiulin Lin, Minghua Chen, S. Joe Qin, Optimal Peak-Minimizing Online Algorithms for Large-Load Users with Energy Storage, paper #1570699920. May 2021
  • Fellow, National Academy of Inventors (NAI). Citation: “have demonstrated a highly prolific spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on the quality of life, economic development, and the welfare of society”, 12/2020
  • 2018 Fellow of American Institute of Chemical Engineers (AIChE)
  • 2015 First Class Academic Award in Natural Science of Liaoning Province, China
  • 2014 Fellow of the International Federation of Automatic Control (IFAC)
  • 2011 Viterbi School of Engineering, The 2011 Northrop Grumman Excellence in Teaching Award
  • 2011 Fellow of IEEE
  • 2007 - 2019 Fluor Professor of Process Engineering
  • 2006 The Ministry of Education of China Cheung Kong (Guest Chair) Professor
  • 2005 The 16th International Federation of Automatic Control (IFAC) World Congress Control Engineering Prize
  • 2003 National Natural Science Foundation of China Distinguished Overseas Young Investigator Award
  • 2003 University of Texas, Paul D. and Betty Robertson Meek and American Petrofina Foundation Centennial Professorship in Chemical Engineering
  • 2003 Student Engineering Council, University of Texas Faculty Appreciation Award
  • 2001 The University of Texas, Faculty Research Assignment Award
  • 2001 University of Texas, Quantum Chemical Corporation Endowed Faculty Fellowship in Engineering
  • 2000 National Science Foundation CAREER Award
  • 2000 University of Texas at Austin, Chevron Teaching Fellowship in Chemical Engineering
  • 1999 University of Texas at Austin, Halliburton/Brown & Root Young Faculty Excellence Award
  • 1999 DuPont Young Professor Award
  • 1997 Alcoa Foundation Award
  • 1997 The University of Texas, Austin, Departmental Teaching Award
  • 1997 University of Texas at Austin, Faculty Excellence Award
  • 1994 Control Engineering Magazine Product Recognition Award

Services outside CityU


  • INFICON, Syracuse, New York. June 19-21, 2019
  • Aspen Academy, Aspen Tech, Inc., January 2017-present
  • Chevron Energy Technology Company, 2012-2013
  • Northern Microelectronics Company, Beijing, China, November 2007-2009
  • Intel, Visiting Professor, May-September 2005
  • Weyerhaeuser, Visiting Professor, June 2004
  • Taiwan Industrial Technology Research Institute, 2003-2006
  • Fisher-Rosemount Systems, Inc., 1995-1997
  • Praxair, Inc., Process Control Scouting, 1996-1998
  • AMD, Inc., 1995-2002
  • Aspen Technology, 1998-2000, 2006