Shiyu Zhou

Vilas Distinguished Achievement Professor

Department of Industrial and Systems Engineering

University of Wisconsin

Madison, WI53706

Phone: 608-262-9534 Fax: 608-262-8454

email: szhou@engr.wisc.edu

Curriculum Vitae

Manufacturing Process Analysis and Control (MPAC) Lab


Education

Working Experiences

  • 08/2017-Present Director, IoT Systems Research Center, University of Wisconsin-Madison

  • 08/2011-Present Professor, Department of Industrial Engineering, University of Wisconsin-Madison

  • 09/2010-Present Associate Chair, Department of Industrial Engineering, University of Wisconsin-Madison

  • 08/2007-08/2011 Associate Professor, Department of Industrial Engineering, University of Wisconsin-Madison

  • 08/2002-07/2007 Assistant Professor, Department of Industrial Engineering, University of Wisconsin-Madison

  • 01/2001-08/2002 Adjunct Assistant Professor, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan.

Research Interests

My research interests are in the area of modeling, monitoring, diagnosis, prognosis, and control of complex engineering systems through data analytics and machine learning.

  • Data driven modeling and control of complex engineering systems: modeling of the variation propagation in large complex manufacturing processes, especially multistage machining process, variation management and tolerance allocation, design for variation reduction, quality control

  • Reliability modeling, prognosis, and maintenance decision making: Degradation signal modeling and forecasting, system survival analysis, condition based maintenance decision making for large scale multi-unit systems.

  • Industrial data analytics and machine learning: data fusion, anomaly detection, feature extraction, predictive analytics, and pattern recognition.

Teaching

  • ISyE 691 Industrial Data Analytics

  • ISyE 510 Facilities Planning

  • ISyE 612 Information Sensing and Data Analysis for Manufacturing

  • ISyE 655 Advanced CAD/CAM

  • ISyE 512 Inspection, Quality control, and Reliability

  • ISyE 605 Computer Integrated Manufacturing

Honors & Awards

  • IISE Fellow, 2017

  • SME Fellow, 2017

  • ASME Fellow, 2015

  • NSF CAREER Award, 2006.

  • IIE Transactions Best Application Paper Award, 2006

  • SME Education Foundation Research Initiation Award, 2003

  • College of Engineering (CoE) Distinguished Achievement Award, University of Michigan, 2000.

Selected Publications

  • Books

  1. Shiyu Zhou and Yong Chen, 2021, Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach, Wiley. Code and data for the book.

  2. Jingshan Li, Shiyu Zhou, Yehui Han, eds, 2016, Advances in Battery Manufacturing, Service, and Management Systems, Wiley.

  • Journal papers

  1. Abhijeet Bhardwaj, Raj Veeramani, Shiyu Zhou, 2022, “Confidently extracting hierarchical taxonomy information from unstructured maintenance records of industrial equipment”, International Journal of Production Research, in press.

  2. Deep, A., Zhou S., Veeramani D. and Chen Y., 2022, “HMM-Based Joint Modeling of Condition Monitoring Signals and Failure Event Data for Prognosis”, IEEE Transactions on Reliability, in press.

  3. Congfang Huang, Jaesung Lee, Yang Zhang, Shiyu Zhou, Jiong Tang, 2022, Mixed-Input Bayesian Optimization Method for Structural Damage Diagnosis, IEEE Transactions on Reliability, In press

  4. Jinwen Sun, Shiyu Zhou and Raj Veeramani, 2022, A Deep Neural Network-based Control Chart for Monitoring and Interpreting Multivariate Autocorrelated Processes using Layer-wise Relevance Propagation, Quality Engineering, DOI: 10.1080/08982112.2022.2087041.

  5. Jinwen Sun, Akash Deep, Shiyu Zhou, Dharmaraj Veeramani, Industrial System Working Condition Identification using Operation-adjusted Hidden Markov Model, Journal of Intelligent Manufacturing, https://doi.org/10.1007/s10845-022-01942-z.

  6. A. S. Bhardwaj, A. Deep, D. Veeramani and S. Zhou, "A Custom Word Embedding Model for Clustering of Maintenance Records," in IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 816-826, Feb. 2022, doi: 10.1109/TII.2021.3079521.

  7. Akash Deep, Shiyu Zhou & Dharmaraj Veeramani (2022) A data-driven recurrent event model for system degradation with imperfect maintenance actions, IISE Transactions, 54:3, 271-285, DOI: 10.1080/24725854.2021.1871687

  8. Jaesung Lee, Chao Wang, Xiaoyu Sui, Shiyu Zhou, Junhong Chen, Landmark-embedded Gaussian process with applications for functional data modeling, 2022, IISE Transactions, 54:11, 1033-1046, DOI: 10.1080/24725854.2021.1974129.

  9. Congfang Huang, Akash Deep, Shiyu Zhou, Dharmaraj Veeramani, A Deep Learning Approach for Predicting Critical Events using Event Logs, Quality Reliability Engineering International. Accepted.

  10. Chao Wang and Shiyu Zhou, Control of key performance indicators of manufacturing production systems through pair-copula modeling and stochastic optimization, Journal of Manufacturing Systems. Accepted.

  11. Akash Deep, Shiyu Zhou & Dharmaraj Veeramani (2020) Copula-based multi-event modeling and prediction using fleet service records, IISE Transactions, DOI: 10.1080/24725854.2020.1802792

  12. Chao Wang, Xiaojin Zhu, Shiyu Zhou & Yingqing Zhou (2020) Bayesian learning of structures of ordered block graphical models with an application on multistage manufacturing processes, IISE Transactions, DOI: 10.1080/24725854.2020.1786196

  13. R. Kontar, G. RASKUTTI and S. Zhou (2020), "Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2020.2987482.

  14. Jaesung Lee, Shiyu Zhou & Junhong Chen (2020) Statistical Modeling and Analysis of k-Layer Coverage of Two-Dimensional Materials in Inkjet Printing Processes, Technometrics, DOI: 10.1080/00401706.2020.1805020

  15. J. Lee, J. Son, S. Zhou and Y. Chen, 2020, "Variation Source Identification in Manufacturing Processes Using Bayesian Approach With Sparse Variance Components Prior," in IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1469-1485, July 2020, doi: 10.1109/TASE.2019.2959605.

  16. Salman Jahani, Raed Kontar, Shiyu Zhou & Dharmaraj Veeramani (2020) Remaining useful life prediction based on degradation signals using monotonic B-splines with infinite support, IISE Transactions, 52:5, 537-554, DOI: 10.1080/24725854.2019.1630868

  17. A. Deep, D. Veeramani and S. Zhou, 2020, "Event Prediction for Individual Unit Based on Recurrent Event Data Collected in Teleservice Systems," in IEEE Transactions on Reliability, vol. 69, no. 1, pp. 216-227, March 2020, doi: 10.1109/TR.2019.2909471.

  18. Junbo Son, Patricia Flately Brennan, Shiyu Zhou, 2020, A Data Analytics Framework for Smart Asthma Management Based on Remote Health Information Systems with Bluetooth-Enabled Personal Inhalers, MIS Quarterly, 44(1b), pp285-303; DOI: 10.25300/MISQ/2020/15092.

  19. Liu, Y., Zhou, S., Chen, Y., and Tang, J. 2019, "Measurements Selection for Bias Reduction in Structural Damage Identification." ASME. J. Dyn. Sys., Meas., Control. March 2019; 141(3): 031003. https://doi.org/10.1115/1.4041505

  20. Chao Wang and Shiyu Zhou, 2019, “Approximate multivariate distribution of key performance indicators through ordered block model and pair copula”, IISE Transactions. 51(11), pp 1265-1278.

  21. Salman Jahani, Raed Kontar, Dharmaraj Veeramani, Shiyu Zhou, 2018, “Statistical Monitoring of Multiple Profiles Simultaneously Using Gaussian Processes”, Quality Reliability Engineering International, 34(8), pp1510-1529.

  22. Raed Kontar, Shiyu Zhou, Chaitanya Sankavaram, Xinyu Du, Yilu Zhang, 2017, “Nonparametric Modeling and Prognosis of Condition Monitoring Signals Using Multivariate Gaussian Convolution Processes”, Technometrics, 60:4, p484-496.

  23. Raed Kontar, Shiyu Zhou, Chaitanya Sankavaram, Member, Xinyu Du, Yilu Zhang, 2017, “Nonparametric Condition Based Remaining Useful Life Prediction Incorporating External Factors”, IEEE Transactions on Reliability, 67(1), p41-72.

  24. Yuhang Liu , Qi Shuai , Shiyu Zhou, and Jiong Tang, 2017, “Prognosis of Structural Damage Growth via Integration of Physical Model Prediction and Bayesian Estimation”, IEEE Transactions on Reliability, 66(3),p700-711.

  25. Chao Wang, Shiyu Zhou, 2017, “Contamination Source Identification Based on Sequential Bayesian Approach for Water Distribution Network with Stochastic Demands”, IISE Transactions, 49(9), pp 899-910.

  26. Chao Wang, Shiyu Zhou, 2017, “Process Tracking and Monitoring Based on Discrete Jumping Model”, Journal of Quality Technology, in press.

  27. Q. Shuai, K. Zhou, Shiyu Zhou, and J. Tang, 2017, “Fault Identification Using Piezoelectric Impedance Measurement and Model-based Intelligent Inference with Pre-screening”, Smart Materials and Structures, 26(4), 045007.

  28. Devashish Das and Shiyu Zhou, 2017, “Estimation of maximum entropy distribution and its application in statistical monitoring of categorical data”, IISE Transactions, 49(8), pp 827-837.

  29. Junbo Son, Patricia Flatley Brennan, Shiyu Zhou, 2016, “Correlated Gamma-based Hidden Markov Model for the Smart Asthma Management based on Rescue Inhaler Usage”, Statistics in Medicine, 36(10), 1619-1637.

  30. Raed Kontar, Junbo Son, Shiyu Zhou, Chaitanya Sankavaram, Yilu Zhang , Xinyu Du, 2016, “Remaining Useful Life Prediction Based on the Mixed Effects Model with Mixture Prior Distribution”, IISE Transactions, 49(7), 682-697.

  31. Raed Kontar, Shiyu Zhou, and John Horst, 2016, “Estimation and Monitoring of Key Performance Indicators of Manufacturing Systems Using the Multi-Output Gaussian Process”, International Journal of Production Research. 55(8), 2304-2319.

  32. Yuan Yuan, Nan Chen, and Shiyu Zhou, 2016, “Modelling Regression Quantile Process using Monotone B-splines”, Technometrics,1-13.

  33. Liu Y, Zhou S, Tang J. 2016, “Identifiability Analysis of Finite Element Models for Vibration Response-Based Structural Damage Detection in Elastic Beams”, Journal of Dynamic Systems, Measurement, and Control, 138(12), 121006.

  34. Jianguo Wu, Yuhang Liu, Shiyu Zhou, 2016, “Bayesian Hierarchical Linear Modeling of Profile Data with Applications to Quality Control of Nanomanufacturing”, IEEE Transactions on Automation Science and Engineering, 13(3), 1355-1366.

  35. Junbo Son , Shiyu Zhou, Chaitanya Sankavaram, Xinyu Du, Yilu Zhang, 2016, “Remaining Useful Life Prediction based on Noisy Condition Monitoring Signals using Constrained Kalman Filter”, Reliability Engineering and System Safety, 152 , 38-50.

  36. Devashish Das, Shiyu Zhou, Yong Chen, and John Horst, 2016, “Statistical monitoring of over-dispersed multivariate count data using approximate likelihood ratio tests”, International Journal of Production Research, 54(21), 6579-6593.

  37. Jianguo Wu , Yong Chen , Shiyu Zhou, Xiaochun Li, 2016, “Online Steady-state Detection Using Multiple Change-point Models and Exact Bayesian Inference”, IISE Transactions, 48(7), 599-613.

  38. Son, J., Patricia Brennan, Zhou, S., 2016, “Rescue Inhaler Usage Prediction in Smart Asthma Management Systems using Joint Mixed Effects Logistic Regression ModelIISE Transactions, 48(4), 333-346.

  39. Qiang Zhou, Tian Jin, Peter Z.G. Qian and Shiyu Zhou, 2016, “Bi-diresctional Sliced Latin Hypercube Designs", Statistica Sinica, 26, 653-674.

  40. Das, D., Chen, Y., Zhou, S., and Sievenpiper, C., 2016, “Monitoring of Multiple Binary Data Streams using a Hierarchical Model Structure”, Quality Reliability Engineering International, 32(4), 1307-1319.

  41. Jianguo Wu, Yong Chen, Shiyu Zhou, Xiaochun Li, 2016, “Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters”, IEEE Transactions on Automation Science and Engineering, 13(2), 688-700.

  42. Yuhang Liu , Jianguo Wu, Shiyu Zhou, Xiaochun Li, 2015, “Microstructure Modeling and Ultrasonic Wave Propagation Simulation of A206–Al2O3 Metal Matrix Nanocomposites for Quality Inspection”, Journal of Manufacturing Science and Engineering, 138(3).

  43. Yuhang Liu, Shiyu Zhou, 2014, “Detecting Point Pattern of Multiple Line Segments Using Hough Transformation”, IEEE Transactions on Semiconductor Manufacturing, accepted.

  44. Son, J., Zhou, Q., Zhou, S., and Salman, M., 2013, “Prediction of the failure interval with maximum power based on remaining useful life distribution,” IIE Transactions, accepted.

  45. Jianguo Wu, Shiyu Zhou , Xiaochun Li, 2013, “Ultrasonic Attenuation Based Inspection Method for Scale-up Production of A206-Al2O3 Metal Matrix Nanocomposites”, Journal of Manufacturing Science and Engineering, in press.

  46. Junbo Son , Yilu Zhang , Chaitanya Sankavaram, Shiyu Zhou, 2014, “RUL Prediction for Individual Units Based on Condition Monitoring Signals with a Change Point”, IEEE Transactions on Reliability, in press.

  47. Devashish Das, Shiyu Zhou, “Statistical Process Monitoring Based on Maximum Entropy Density Approximation and Level Set Principle”, IISE Transactions, in press.

  48. Qingbo He and Shiyu Zhou, 2014, Discriminant Locality Preserving Projection Chart for Multivariate Statistical Process Control, International Journal of Production Research, in press.

  49. Qiang Zhou, Son, J., Shiyu Zhou, Xiaofeng Mao, Mutasim Salman, 2013, Remaining Useful Life Prediction of Individual Units Subject to Hard Failure, IIE Transactions, accepted

  50. Chen, N., and Zhou, S., 2014, “A CUSUM Scheme for Statistical Monitoring of Queueing Systems”, Technometrics, in press.

  51. Jianguo Wu, Shiyu Zhou, Xiaochun Li, 2013, Acoustic Emission Monitoring for Ultrasonic Cavitation Based Dispersion and Homogenization Process, ASME Transactions, Journal of Manufacturing Science and Engineering, 135(3), 031015.1-12.

  52. Junbo Son, Qiang Zhou, Shiyu Zhou, Xiaofeng Mao, Mutasim Salman, 2013, Evaluation and Comparison of Failure Prediction Performance of Prognostic Models Based on Degradation Signals and Time-to-failure Data, IEEE Transactions on Reliability, 62(2), 379-394.

  53. Qiang Zhou, Junyi Zhou, Michael Cicco, Shiyu Zhou, Xiaochun Li, 2013, Detecting Particle-Clustering in Nanocomposites Using Microscopic Image Samples, Technometrics, DOI:10.1080/00401706.2013.804440.

  54. Heping Liu, Shiyu Zhou, Xiaochun Li, 2012, Inferring the Size Distribution of 3D Particle Clusters in Metal Matrix Nanocomposites, ASME Transactions, Journal of Manufacturing Science and Engineering, 135(1), 011013.1-9.

  55. Q. Zhou, L. Zeng, M. DeCicco, X. Li, S. Zhou, 2012, “A Comparative Study on Clustering Indices for Distribution Uniformity of Nanoparticles in Metal Matrix Nanocomposites”, CIRP Journal of Manufacturing Science and Technology, 5(4), 348-356.

  56. Yuan, Y. and Zhou, S., 2012, “Adaptive Knot Selection for B-spline Curve Fitting Using Multi-Resolution Basis Set”, IIE Transactions, accepted.

  57. Devashish Das, Shiyu Zhou, John Lee, 2012, Differentiating Alcohol Induced Driving Behavior Using Steering Wheel Signals, IEEE Transactions on Intelligent Transportation Systems, vol.13, no.3, pp.1355-1368.

  58. Yuan, Y. and Zhou, S., 2012, Sequential B-spline Surface Construction Using Multi-Resolution Data Cloud, ASME Transaction, Journal of Computing and Information Science in Engineering. 12, 021008 (2012).

  59. Chen, N., Yuan, Y., and Zhou, S., 2011, “Performance analysis of the monitoring of the queue length data monitoring in of M/G/1 queues”, Naval Research Logistics, 58(5), DOI 10.1002/nav.

  60. Zhou, Q., Qiang, Z.G.P., and Zhou, S., 2012, “Surrogate Modeling of Multistage Assembly Processes Using Integrated Emulation”, ASME Transactions, Journal of Mechanical Design. 134(1), doi:10.1115/1.4005440.

  61. 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), DOI:10.1080/0740817X.2011.635180.

  62. Zeng, L. and Zhou, S., 2011, “A Bayesian Approach for Risk-adjusted Outcome Monitoring in Healthcare”, Statistics in Medicine. DOI: 10.1002/sim.4374 .

  63. Li, Z. and Zhou, S., Sievenpiper C., and Choubey S., 2011, "Statistical Monitoring of Time-to-Failure Data Using Rank Tests”, Quality Reliability Engineering International, DOI: 10.1002/qre.1248.

  64. Zhou, Q., Qian, ZG, Zhou, S., 2011, “A Simple Approach to Emulation for Computer Models with Qualitative and Quantitative Factors”, Technometrics, 53(3), pp. 266-273.

  65. Yuan, Y., Zhou, S., Manar, K., Zheng, Y., Sievenpiper C., 2011, “Event Log Modeling and Analysis for System Failure Prediction”, IIE Transactions. 43(9), pages 647-660.

  66. Chen, N. and Zhou, S., 2010, “Simulation-Based Cycle Time Quantile Regression on System Throughputs”, IIE Transactions, 43(3) pg:176-191.

  67. Li, Z. and Zhou, S., Sievenpiper C., and Choubey S., 2010, “On Measuring Differences in Evolving Discrete Event Sequences under the Cox Proportional Hazards Model”, Quality Reliability Engineering International, 26, pp677-689.

  68. Chen N., Chen Y., Li Z., Zhou S., Sievenpiper C., 2010, “Optimal Variability Sensitive Condition-based Maintenance based on System Event Logs”, International Journal of Production Research. DOI: 10.1080/00207541003694811

  69. Zhou, Q. and Zhou, S., 2010, “Statistical Detection of Linear Defect Patterns Using Hough Transform”, IEEE Transactions on Semiconductor Manufacturing, 23(3), pp370-380.

  70. Loose, J., Zhou, Q., Zhou, S., Ceglarek, D., 2009, “Dimensional Variation Modeling for Multistage Machining Processes Incorporating Part GD&T Characteristics”, International Journal of Production Research, DOI: 10.1080/00207540802691366.

  71. Shi, J. and Zhou S., 2009, Quality control and improvement for multistage systems: A survey, IIE Transactions, 41(9), pp 744 – 753.

  72. Loose, J., Chen, N., and Zhou, S. (2009), "Surrogate modeling of dimensional variation propagation in multistage assembly process", IIE Transaction, 41(10), p893-904.

  73. Chen, N., and Zhou, S., 2009, "Detectability study for statistical monitoring of multivariate dynamic processes", IIE Transaction, 41, 593-604.

  74. Loose, J., Zhou, S. and Ceglarek, D., 2008, “Variation Source Identification in Manufacturing Processes Based on Relational Measurements of Key Product Characteristics”, Journal of Manufacturing Science and Engineering, 130, 1-11.

  75. Zeng, L., Jin, N., and Zhou, S., 2008, "Multiple Fault Signature Integration and Enhancing for Variation Source Identification in Manufacturing Processes", IIE Transactions, 40, pp. 919–930.

  76. Hao, S., Zhou, S., and Ding, Y., 2008, "Multivariate process variability monitoring through projection based on a process model", Journal of Quality Technology, 40(2), pp214-226.

  77. Chen, N., Zhou, S., Chang, T., and Huang, H., 2008, “Attribute Control Charts Using Generalized Zero-Inflated Poisson Distribution”, Quality Reliability Engineering International, 24(7), pp.793-806.

  78. Zeng, L. and Zhou, S., 2008, “Impacts of Measurement Errors and Regressor Selection on Regression Adjustment Monitoring of Multistage Manufacturing Processes”, IIE Transactions. 40, pp.109-121.

  79. Zeng, L. and Zhou, S., 2007, “Building Direct Influence Graph for Manufacturing Processes with Complex Topologies”, Technometrics, 49(4), pp373-381.

  80. Li, Z., Wu, T., and Zhou, S., 2007, “Statistical Detection of Process and Sensor Faults for Manufacturing Quality Control”, Transactions of the NAMRI/SME, Volume 35, pp247-254

  81. Jin, N., Zhou, S., Chang, and Huang, 2006, “Influential Process Variable Selection for Surface Quality Control in Hot Rolling Processes”, IEEE Transactions on Automation Science and Engineering, 5(3), pp557-562.

  82. Li Z., Zhou S., Choubey S., and Sievenpiper C., 2007, “Failure Event Prediction Using Cox Proportional Hazard Model Driven by Frequent Failure Signatures”, IIE Transactions on Quality and Reliability Engineering, 39, 303–315.

  83. Li, Z., Zhou, S., Ding, Y., 2007, “Pattern Matching for Root Cause Identification of Manufacturing Processes with Consideration of General Structured Noise”, IIE Transactions on Quality and Reliability Engineering, 39, 251–263

  84. Loose, J., Zhou, S., Ceglarek, D., 2007, “Kinematic Analysis of Dimensional Variation Propagation for Multistage Machining Processes with General Fixture Layouts”, IEEE Transactions on Automation Science and Engineering, 4(2), 141-152.

  85. Ren Y., Ding, Y., and Zhou, S., 2006, “A Data-mining Approach to Study the Significance of Nonlinearity in Multi-Station Assembly Processes”, IIE Transactions on Quality and Reliability Engineering, 38, pp.1069-1083.

  86. Jin, N. and Zhou, S., 2006, “Data-Driven Variation Source Identification of Manufacturing Processes Based on Eigenspace Comparison”, Naval Research Logistics, 53(5), pp383-396.

  87. Ding, Y., Zeng, L., and Zhou, S., 2005, “Phase I Analysis for Monitoring Nonlinear Profile Signals in Manufacturing Processes”, Journal of Quality Technology, 38(3), 199-216.

  88. Jin, N. and Zhou, S., 2006, "Signature Construction and Matching for Fault Diagnosis in Manufacturing Processes through Fault Space Analysis", IIE Transactions, 38, pp.341-354, IIE Transactions Best Application Award.

  89. Zhou, S., Sun, B., Shi, J., 2006, "An SPC Monitoring System for Cycle-Based Waveform Signals Using Haar Transform", IEEE Transactions on Automation Science and Engineering, 37, pp971-982.

  90. Li, Z., Zhou, S., 2006, “Robust Method of Multiple Variation Sources Identification in Manufacturing Processes for Quality Improvement”, ASME Transactions, Journal of Manufacturing Science and Engineering, 128(1), pp326-336.

  91. Jin, J., Guo, H., and Zhou, S., 2006, “Supervisory Generalized Predictive Control Combining with Statistical Process Control for Thin Film Deposition Processes”, Journal of Manufacturing Science and Engineering, 128(1), pp315-325.

  92. Zhou, S., Jin, N., Jin, J., 2005, “A New Directional Variant Multivariate Control Chart System Considering Known Process Faulty Conditions”, IIE Transactions, 37, pp971-982.

  93. Zhou, S. and Jin, J., 2005, “An Unsupervised Clustering Method For Cycle-Based Waveform Signals In Manufacturing Processes”, IIE Transactions on Quality and Reliability Engineering, 37, pp.569-584.

  94. Ding, Y., Zhou, S., and Chen, Y., 2005, "A Comparison of Process Variation Estimators for In-Process Dimensional Measurements and Control", ASME Transactions, Journal of Dynamic Systems, Measurement and Control, 127, pp69-79.

  95. Ceglarek, D., Huang, W., Zhou, S., Ding, Y., Kumar, R., and Zhou, Y., 2004, “Time-Based Competition in Multistage Manufacturing, Stream-of-Variation Methodology (SOVA) – A Review”, International Journal of Flexible Manufacturing Systems, 16, 11-44.

  96. Jin, N., Zhou, S., and Chang, T., 2004, “Identification of impacting factors of surface defects in hot rolling processes using multi-level regression analysis”, Transactions of NAMRI/SME, 32, pp.557-564.

  97. Zhou, S., Chen, Y., and Shi, J., 2004, “Root Cause Estimation and Statistical Testing for Quality Improvement of Multistage Manufacturing Processes”, IEEE Transactions on Automation Science and Engineering, 1(1), pp73-83.

  98. Zhou, S., Shin, K., Dyer, S., Shi, J., Ni, J., 2004, "Extended Influence Coefficient Method for Rotor Active Balancing during Acceleration", ASME Transactions, Journal of Dynamic Systems, Measurement and Control, 126, pp219-223.

  99. Zhou, S. and Shi, J., 2004, "Identification of Nonlinear Effects in Rotor Systems Using Recursive QR Factorization Method", Journal of Sound and Vibration, 270, pp.455 –469.

  100. Zhou, S., Ding, Y., Chen, Y., Shi, J., 2003, "Diagnosability study of multistage manufacturing processes based on linear mixed-effects models", Technometrics, 45(4), pp312~325.

  101. Zhou, S., Huang, Q., Shi, J., 2003, "State Space Modeling of Dimensional Variation Propagation in Multistage Machining Process Using Differential Motion Vectors", IEEE Transactions on Robotics and Automation. 19(2),pp296-309.

  102. Huang, Q., Zhou, S., Shi, J., 2002, "Diagnosis of Multi-Operational Machining Processes by Using Virtual Machining", Robotics and Computer Integrated Manufacturing, 18, pp.233 –239.

  103. Zhou, S. and Shi, J., 2002, "Optimal One-Plane Active Balancing of Rigid Rotor during Acceleration", Journal of Sound and Vibration, 249(1), pp.196-205.

  104. Zhou, S. and Shi, J., 2001, "Active Balancing and Vibration Control of Rotating Machinery: A Survey", The Shock and Vibration Digest, 33(5), pp361-371.

  105. Zhou, S. and Shi, J., 2001, "Imbalance Estimation for Speed-Varying Rigid Rotors Using Time-Varying Observer", ASME Transactions, Journal of Dynamic Systems, Measurement, and Control, 123, pp637-644.

  106. Zhou, S. and Shi, J., 2001, "The Analytical Unbalance Response of Jeffcott Rotor during Acceleration", ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 123, No. 2, pp299-302.

  107. Zhou, S. and Shi, J., 2000, "Supervisory Adaptive Balancing of Rigid Rotors during Acceleration", Transactions of NAMRI/SME. Vol. XXVII, pp425-430.

  108. Chirikjian, G. S. and Zhou, S., 1998, "Metrics on Motion and Deformation of Solid Models", ASME Transactions, Journal of Mechanical Design, Vol. 120, pp252-261.


P.h.D Graduates

  1. Jaesung Lee, 2022, "Data-Driven Variation Modeling And Management With Application Of Advanced Manufacturing Processes And Systems", Current Position: Assistant Professor, Texas A&M University

  2. Akash Deep, 2022, "Data-driven Modeling, Prognosis, and Control of Discrete Events in Smart and Connected Systems", (Co-Chair with Raj Veeramani), Current Position: Assistant Professor, Oklahoma State University

  3. Salman Jahani, 2021, "Monitoring, Prognosis and Decision-Making for Internet of Things Enabled Systems", (Co-Chair with Raj Veeramani) Current Position: SAP

  4. Chao Wang, 2019, "Data Driven Modeling, Monitoring and Control for Smart and Connected Systems".
    Current Position:
    Assistant Professor, Department of Industrial and Systems Engineering, University of Iowa.

  5. Raed Al Kontar, 2018, "Data-driven Modeling and Prognosis of Condition Monitoring Signals in Engineering Systems".
    Current Position:
    Assistant Professor, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor.

  6. Yuhang Liu, 2017, "Data Analytics Models and Methods for Fault Identification and Prognosis in Mechanical Structures and Manufacturing Processes".
    Current Position: Senior Data Scientist, GNS Healthcare, Cambridge, MA.

  7. Junbo Son, 2016, "Data-driven Prognosis and Diagnosis of Event Occurrences with Applications in Manufacturing and Healthcare Systems".
    Current Position:
    Assistant Professor, Department of Business Administration (Operations Management), University of Delaware.

  8. Devashish Das, 2015,"Statistical Monitoring Methods based on Hierarchical Statistical Models and Information Theoretic Measures".
    Current Position:
    Research Staff, Amazon.

  9. Jianguo Wu, 2015,"Statistical Analysis, Monitoring and Control of the Production of High Performance Lightweight Metal-matrix Nanocomposites".
    Current Position:
    Assistant Professor, Department of Industrial Engineering and Management, Peking University, Beijing, China.

  10. Yuan Yuan, 2014,"Nonparametric Modeling and Analysis Using B-splines with Industrial Applications".
    Current Position: Reseach Staff,
    IBM Research Center, Singapore.

  11. Qiang Zhou , 2011,"Computer Simulation Driven Statistical Modeling and Quality Control".
    Current Position:
    Assistant Professor , Department of Systems and Industrial Engineering, University of Arizona.

  12. Nan Chen, 2010, "Discrete Event Modeling and Analysis: With Applications in Production and Service Systems".
    Current Position:
    Associate Professor , Department of Industrial and Systems Engineering, National University of Singapore.

  13. Li Zeng, 2009, "On Change Detection in Manufacturing and Service Systems".
    Current Position:
    Associate Professor , School of Data Science, City University of Hong Kong.

  14. Jean-Philippe Loose, 2008, "Physical and surrogate modeling for complex manufacturing process design and control".
    Current Position: Marketing Intelligence,
    Facebook, California.

  15. Zhiguo Li, 2007, "Continuous- and Discrete-Signature Driven Fault Management Methodologies for Complex Engineering Systems".
    Current Position: Research Staff,
    IBM Research Center, New York.

  16. Nong Jin, 2006, "Data-driven self-improving fault detection and diagnosis methodologies in complex manufacturing process".
    Current Position: Senior data analyst,
    Capital One Financial Corp.

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