Qiang Zhou

Qiang Zhou
Assistant Professor
520.621.3331
zhouq@email.arizona.edu

Education

  • PhD, Industrial Engineering, University of Wisconsin, Madison, 2011
  • MS, Statistics, University of Wisconsin, Madison, 2010
  • MS, Mechanical Engineering, Tsinghua University, Beijing, China, 2007
  • BE, Automotive Engineering, Tsinghua University, Beijing, China, 2005

Research and Teaching Interests

  • Industrial data analytics 
  • System informatics and applied statistics 
  • Design analysis of computer experiments 
  • System fault management and prognostics 
  • Statistical quality control

Selected Publications

  • Li, J., and Zhou, Q. (2017), “A General Approach for Monitoring Autocorrelated Categorical Processes,” Journal of Quality Technology, accepted. 
  • Li, Y., Zhou, Q., Huang, X., Zeng, L. (2017), “Pairwise Estimation of Multivariate Gaussian Process Models With Replicated Observations: Application to Multivariate Profile Monitoring”, Technometrics, tentatively accepted. 
  • Huang, X., Xu, J., Zhou, Q. (2017), “Diagnosis of Multi-scale Spatial Point Interaction via Decomposition of the K Function-based T2 Statistic,” Journal of Quality Technology, accepted. 
  • Li, Y., and Zhou, Q. (2016), “Pairwise Meta-Modeling of Multivariate Output Computer Models Using Nonseparable Covariance Function,” Technometrics, 58(4), 483-494. 
  • Zhou, Q., Jin, T., Qian, P.Z.G., and Zhou, S. (2016), “Bi-directional Sliced Latin Hypercube Designs,” Statistica Sinica, 26(2), 653-674. 
  • Huang, X., Zhou, Q., Zeng, L., and Li, X. (2016), “Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function,” IEEE Transactions on Automation Science and Engineering, 10.1109/TASE.2015.2479088. 
  • Son, J., Zhou, Q., Zhou, S., Salman, M. (2015), “Prediction of the Failure Interval With Maximum Power Based on Remaining Useful Life Distribution,” IIE Transactions, 47(10), 1072-1087. 
  • Zhou, Q., Zhou, S., Mao, X., Salman, M. (2014), “Remaining Useful Life Prediction of Individual Units Subject to Hard Failure,” IIE Transactions, 46(10), 1017-1030. 
  • Zhou, Q., Zhou, J., DeCicco, M., Li, X., and Zhou, S. (2014), “Detecting 3D Spatial Clustering of Particles in Nanocomposites Based on Cross-sectional Images,” Technometrics, 56(2), 212-224. 
  • Son, J., Zhou, Q., Zhou, S., Mao, X., Salman, M. (2013) “Evaluation and Comparison of Mixed Effects Model Based Prognosis for Hard Failure,” IEEE Transactions on Reliability, 62(2), 379-394. 
  • Zhou, Q., Zeng, L., DeCicco, M., Li, X., and Zhou, S. (2012), “A Comparative Study on Clustering Indices for Distribution of Nanoparticles in Metal Matrix Nanocomposites,” CIRP Journal of Manufacturing Science and Technology, 5(4), 348-356. 
  • Zeng, L., Zhou, Q., De Cicco, M., Li, X., and Zhou, S. (2012), “Quantifying Boundary Effect of Nanoparticles in Metal Matrix Nanocomposite Fabrication Processes,” IIE Transactions, 44(7), 1-17. 
  • Zhou, Q., Qian, P.Z.G., and Zhou, S. (2011), “A Simple Approach to Emulation for Computer Models With Qualitative and Quantitative Factors,” Technometrics, 53(3), 266–273. 
  • Zhou, Q., Qian, P.Z.G., Zhou, S. (2012), “Surrogate Modeling of Multistage Assembly Processes Using Integrated Emulation,” ASME Transactions, Journal of Mechanical Design, 134(1), 011002. 
  • Zhou, Q., Zeng, L., and Zhou, S. (2010), “Statistical Detection of Defect Patterns Using Hough Transform,” IEEE Transactions on Semiconductor Manufacturing, 23(3), 370-380. 

University of Arizona College of Engineering