Zhenyu "James" Kong

Associate Professor

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Research Areas

Main Area: Manufacturing Systems Engineering

  • Sensing and analytics for smart manufacturing
  • Modeling, synthesis, and diagnosis for large and complex manufacturing systems
  • Data mining and machine learning for manufacturing and service applications
  • Ph.D., Industrial and Systems Engineering, University of Wisconsin-Madison, 2004
  • M.S., Mechanical Engineering, Harbin Institute of Technology, China, 1995
  • B.S., Mechanical Engineering, Harbin Institute of Technology, China, 1993
  • Associate Professor, Department of Industrial and Systems Engineering, Virginia Tech, 08/2013 – present.
  • Associate Professor, School of Industrial Engineering and Management, Oklahoma State University, 07/2012 – 08/2013.
  • Assistant Professor, School of Industrial Engineering and Management, Oklahoma State University, 08/2006 - 06/2012.
  • Senior Research Engineer, Dimensional Control Systems, Troy, Michigan, 07/2004 - 07/2006.
  • Research Assistant, University of Wisconsin-Madison, 09/2000 - 07/2004.
  • Research Fellow, University of Michigan-Ann Arbor, 11/1998 - 09/2000.
  • ISE 3214:  Facilities and Logistics
  • ISE 6284:  Advanced Topics in Manufacturing Systems Engineering
  • ISE 5984:  Sensing and Data Analytics for Complex Systems
  • ISE 4984:  Data Analytics in Manufacturing and Service Systems
  • ISE 2214:  Manufacturing Process Laboratory
  • ISE 4404:  Statistical Quality Control

Please click a title in brown to view the abstract.

  1. Barazandeh, B., Bastani, K., Rafieisakhaei, M., Kim, S., Kong, Z., and Nussbaum, M., 2017, “Robust Sparse Representation based Classification using Online Sensor Data for Monitoring Manual Material Handling Tasks,” IEEE Trans. Journal of Automation Science and Engineering (in press).
  2. Tootooni, S., Dsouza, A., Donovan, R., Rao, P., Kong, Z., and Borgesen, P., 2017, “Classifying the Dimensional Variation in Additive Manufactured Parts from Laser-Scanned 3D Point Cloud Data using Machine Learning Approaches,” ASME Trans. Journal of Manufacturing Science and Engineering, Vol. 139, No. 9, pp. 091005-1 – 091005-14, DOI: 10.1115/1.4036641
  3. Liu, J., Beyca, O., Rao, P., Kong, Z., and Bukkapatnam, S., 2017, “Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization,” IEEE Trans. Journal of Automation Science and Engineering, Vol. 14, No. 1, pp. 208-221.
  4. Tootooni, M., Rao, P., Chou, C., and Kong, Z., 2016, “A Spectral Graph Theoretic Approach for Monitoring Multivariate Time Series Data From Complex Dynamical Processes,” IEEE Trans. Journal of Automation Science and Engineering, DOI: 10.1109/TASE.2016.2598094.
  5. Bastani, K., Kim, W., Kong, Z., Nussbaum, M., and Huang, W., 2016, “Online Classification and Sensor Selection Optimization with Applications to Human Material Handling Tasks Using Wearable Sensing Technologies,” IEEE Trans Journal of Human-Machine Systems, Vol. 46, No., 4, pp. 485-497, DOI: 10.1109/THMS.2016.2537747.
  6. Bastani, K., Rao, P., and Kong, Z., 2016, “An Online Sparse Estimation based Classification Approach for Real-time Monitoring in Advanced Manufacturing Processes from Heterogeneous Sensor Data,” IIE Trans. Vol. 48, No. 7, pp. 579-598, DOI: 10.1080/0740817X.2015.1122254.

Go to Journal Publications and Conference Papers on my website for other publications.

  • National Science Foundation
  • Department of Transportation
  • National Institute of Standards and Technology
  • Commonwealth Center for Advanced Manufacturing
  • Member of Institute of Industrial Engineers (IIE)
  • Member of Institute for Operation Research and the Management Sciences (INFORMS)
  • Member of American Society of Mechanical Engineering (ASME)
  • Member of Society of Manufacturing Engineers (SME)
  • Member of Institute of Electrical and Electronics Engineers (IEEE)

Zhenyu "James" Kong

Zhenyu "James" Kong
Zhenyu "James" Kong
  • (540) 231-9762
  • zkong@vt.edu
  • 123 Durham Hall
    (MC 0118)
    1145 Perry Street
    Blacksburg, VA 24061

Research Lab

Sensing and Analytics for Smart Manufacturing
(Durham Hall 191)