Data Science Seminar

  • March 23, 2023
  • 4:00 PM - 5:00 CST
  • Cuneo 312
  • Nan Miles Xi, mxi1@luc.edu
  • Not open to the public.
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  • Details

    Title:
    Design, Modeling, and Active Learning for Computer Experiments

    Abstract:
    Computer experiments are simulations or mathematical models that are run on a computer to study the behavior of a system. These experiments have found wide applications in science and engineering when it is either too expensive or impossible to conduct experiments in the physical world. In this talk, I will provide a comprehensive introduction to the methodology used in designing and analyzing computer experiments. Specifically, I will focus on two key techniques: surrogate modeling for analyzing computer experiments, and space-filling designs for collecting data from these experiments. In some cases, the input space of a system may be highly complex, or the output may be heterogeneous, making it difficult to analyze using a one-stage design. Active learning becomes essential for these cases. I will also introduce some active learning techniques used in computer experiments, such as entropy-based sampling and uncertainty sampling.

    Bio:
    Dr. Lin Wang is an Assistant Professor of Statistics at Purdue University. Prior to joining Purdue in 2022, she was an Assistant Professor of Statistics at George Washington University from 2019 to 2022. She obtained her PhD in Statistics in 2019 from University of California, Los Angeles. Her research interests include computer experiments, experimental design and sampling, and causal inference.