Xuan Bi

Xuan Bi

Assistant Professor
Information & Decision Sciences

Contact

3-233 Carlson School of Management

Education:

  • BS 2011
    Mathematics, Tsinghua University
  • PhD 2016
    Statistics, University of Illinois at Urbana-Champaign
  • Postdoctoral Associate 2018
    Biostatistics, Yale University

Expertise:

  • Statistics & Data Science
  • Machine Learning
  • Big Data Analytics
  • Personalization
  • Recommender Systems
  • Data Privacy
  • Imaging Data Analysis

Biography

Dr. Xuan Bi is an Assistant Professor of Information and Decision Sciences in the Carlson School of Management at the University of Minnesota. His general research goal is to design data-driven solutions and provide decision support to benefit organizations and individuals. Dr. Xuan Bi's research mainly focuses on the interface of Statistics, Machine Learning, and Information Systems, which aims at creating and applying data-driven methodologies to address real-world, large-scale, business, and scientific problems. Specifically, Dr. Xuan Bi's works revolve around personalization, with a special interest in recommender systems and data privacy. His works have been published in leading academic journals, including Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, and INFORMS Journal on Computing.

Dr. Xuan Bi holds a Bachelor of Science in Mathematics from Tsinghua University, and a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign. During his doctoral training, Dr. Xuan Bi provided statistical consultation to over 60 clients in academia and industry, in the fields of marketing, precision medicine, finance, law, accounting, biology, engineering, operation, psychology, and sociology. Prior to joining the University of Minnesota, Dr. Xuan Bi was a Postdoctoral Associate at Yale University.


Selected Works & Activities.

  • Journal Articles
    Bi, X., Feng, L., Li, C. and Zhang, H. (2021). Modeling pregnancy outcomes through sequentially nested regression models. Journal of the American Statistical Association.
  • Journal Articles
    Bi, X., Adomavicius, G., Li, W. and Qu, A. (2021). Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness. INFORMS Journal on Computing.
  • Journal Articles
    Zhang, Y., Bi, X., Tang, N.-S. and Qu, A. (2021). Dynamic tensor recommender systems. Journal of Machine Learning Research.
  • Journal Articles
    Feng, L., Bi, X. and Zhang, H. (2020). Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. Journal of the American Statistical Association.
  • Journal Articles
    Tang, X., Bi, X. and Qu, A. (2020). Individualized Multilayer Tensor Learning with An Application in Imaging Analysis. Journal of the American Statistical Association.
  • Journal Articles
    Bi, X., Qu, A. and Shen, X. (2018). Multilayer tensor factorization with applications to recommender systems. Annals of Statistics, 46, 3308-3333.
  • Journal Articles
    Bi, X., Qu, A., Wang, J. and Shen, X. (2017). A group-specific recommender system. Journal of the American Statistical Association, 112, 1344-1353. -Winner of ASA Student Paper Award (SLDS Section, 2016)

Edit This Page

Edit Profile