Assistant Professor of Systems and Industrial Engineering
Assistant Professor, Statistics Graduate Interdisciplinary Program
His research focuses on industrial data analytics for engineering decision making and system performance improvement,using methodologies from applied statistics, data mining, machine learning and signal processing. Specifically, he has been working in the areas such as experimental design and analysis of computer simulations, engineering system monitoring, fault diagnostics and failure prognostics, statistical quality control for manufacturing processes.
- Ph.D. Industrial Engineering
- Univ of Wisconsin - Madison, Madison, Wisconsin, United States
- M.S. Statistics
- Univ of Wisconsin - Madison
- M.S. Mechanical Engineering
- Tsinghua University, Beijing
- B.S. Automotive Engineering
- Tsinghua University, Beijing
- Assistant Profesor, University of Arizona (2017 - Ongoing)
- Assistant Profesor, City University of Hong Kong (2012 - 2016)
- Research Associate, University of Wisconsin - Madison (2011 - 2012)
Industrial data analytics;System informatics and applied statistics;Design and analysis of computer experiments;System fault management and prognostics;Statistical quality control
- Chen, X. i., & Zhou, Q. (2017). Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 262(2), 575-585.
- Huang, X., Xu, J., & Zhou, Q. (2017). Multi-Scale Diagnosis of Spatial Point Interaction Via Decomposition of the K Function-Based T-2 Statistic. JOURNAL OF QUALITY TECHNOLOGY, 49(3), 213-227.
- Huang, X., Zhou, Q., Zeng, L. i., & Li, X. (2017). Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 14(2), 1031-1041.
- Li, J., & Zhou, Q. (2017). A General Approach for Monitoring Serially Dependent Categorical Processes. JOURNAL OF QUALITY TECHNOLOGY, 49(4), 365-379.
- Wang, D., Zhou, Q., & Tsui, K. (2017). On the distribution of the modulus of Gabor wavelet coefficients and the upper bound of the dimensionless smoothness index in the case of additive Gaussian noises: Revisited. JOURNAL OF SOUND AND VIBRATION, 395, 393-400.
- Li, Y., & Zhou, Q. (2016). Pairwise Meta-Modeling of Multivariate Output Computer Models Using Nonseparable Covariance Function. TECHNOMETRICS, 58(4), 483-494.
- Liu, Y. u., Shi, Y. i., Zhou, Q., & Xiu, R. (2016). A sequential sampling strategy to improve the global fidelity of metamodels in multi-level system design. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 53(6), 1295-1313.
- Wang, D., Tsui, K., & Zhou, Q. (2016). Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 72-73, 80-91.
- Wang, D., Yang, F., Tsui, K., Zhou, Q., & Bae, S. J. (2016). Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 65(6), 1282-1291.
- Yang, W., Zhou, Q., & Tsui, K. (2016). Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 54(15), 4703-4721.
- Zhou, Q., Jin, T., Qian, P., & Zhou, S. (2016). BI-DIRECTIONAL SLICED LATIN HYPERCUBE DESIGNS. STATISTICA SINICA, 26(2), 653-674.
- Son, J., Zhou, Q., Zhou, S., & Salman, M. (2015). Prediction of the failure interval with maximum power based on the remaining useful life distribution. IIE TRANSACTIONS, 47(10), 1072-1087.
- Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and Health Management: A Review on Data Driven Approaches. MATHEMATICAL PROBLEMS IN ENGINEERING.
- Chen, X. i., & Zhou, Q. (2014). SEQUENTIAL EXPERIMENTAL DESIGNS FOR STOCHASTIC KRIGING. PROCEEDINGS OF THE 2014 WINTER SIMULATION CONFERENCE (WSC), 3821-3832.
- He, Q., Wang, X., & Zhou, Q. (2014). Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis. SENSORS, 14(1), 382-402.