64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Design and Analysis of Experiments for Data Science


Dr Chunfang Devon Lin


  • CL
    Prof. Chunfang Devon Lin
  • SM
    Dr Simon Mak
  • JS
    Dr John Stufken
  • RL
    Ryan Lekivetz
  • Category: International Society for Business and Industrial Statistics (ISBIS)


    Experimental design and analysis have wide generality and significant advantages for gaining attractive inferential and computational properties. For example, as extraordinary amounts of data are being produced in many branches of science, proven statistical methods are no longer applicable with extraordinary large datasets due to computational limitations. A critical step in big data analysis is data reduction, which is an experimental design problem. Many newly developed methodology in this field have important applications in data sciences. This session aims to cover some representative work on relevant practical problems., such as multi-stage multi-fidelity Gaussian process model for computer experiments, subdata selection for data reduction in data science, variational inference for computation efficiency in data science, optimal designs for nonlinear model in data science.

    We hope this session will help facilitate cross-fertilization of experimental design and analysis and data science. Beyond the design of experiments and statistical learning communities, this session is expected to attract significant attentions from audience in broad fields of statistics and computer science. The tentative titles for the talks are listed as follows:

    1. Dr. John Stufken, Professor, Department of Statistics, School of Computing of the College of Engineering and Computing, George Mason University, USA

    Title: Subdata Selection from Big Data with a Large Number of Variables
    2. Dr. Simon Mak, Assistant Professor, Department of Statistical Science, Duke University, USA

    Title: Design and Analysis of Multi-stage Multi-fidelity Computer Experiments

    3. Dr. Lulu Kang, Associate Professor, Department of Applied Mathematics, Applied Mathematics, Illinois Institute of Technology, USA

    Title: Energetic Variational Inference with Non-Local Interaction

    4. Dr. Luc Pronzato, DR CNRS, Laboratoire I3S - Sophia Antipolis, France
    Title: Optimal designs for nonlinear model in data science

    Session Format: Chair, 4 speakers, and 1 discussant