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  • [ Janurary 06, 2020]

    Dynamic Latent Variable Regression for Inferential Sensor Modeling and Supervised Monitoring

  • Speaker:
    Qinqin Zhu
    Jan. 9th, 2020
    Room 805, Wangkezhen Building
    Xi Zhang
  • Abstract
  • This seminar will be on system modeling and process monitoring with multivariate statistical methods. Specifically, the presentation will (1) review some existing multivariate statistical approaches for process modeling and monitoring; and (2) introduce a novel latent variable regression algorithm and its dynamic counterpart.
  • Biography
  • Dr. Qinqin Zhu is an assistant professor in the department of Chemical Engineering at the University of Waterloo. She is also a faculty member in the Waterloo Artificial Intelligence Institute (Waterloo.AI) and Waterloo Institute for Sustainable Energy (WISE). Dr. Zhu received her master and PhD degrees from the Computer Science department and the Chemical Engineering department respectively, both at the University of Southern California. Prior to UW, She worked as a senior Research Scientist at Facebook Inc. in the United States.

    Dr. Zhu's research mainly focuses on developing advanced statistical machine learning methods, process data analytics techniques and optimization algorithms in the era of big data with applications to statistical process monitoring and fault diagnosis. Her research addresses theoretical challenges and problems of practical importance in the area of process systems engineering. By leveraging the power of mathematical modeling and optimization, her group strives to develop advanced multivariate statistical analysis algorithms that enhance decision making in complex engineering systems.