LI, William
Professor of Management
  • Professor William Li is currently a professor of management at the Shanghai Institute of Advanced Finance (SAIF) at Shanghai Jiao Tong University. Prior to joining SAIF in 2018, Professor Li was a tenured full professor and Eric Jing Professor in the Carlson School of Management at the University of Minnesota. He was also an invited professor at the School of Management at Fudan University.

    Professor William Li's papers are published in leading journals in the field of applied statistics, especially in the top journals recognized in the field of experimental design (e.g., Technometrics, Journal of the American Statistics Association). He is co-author of a widely used statistics textbook, “Applying Linear Statistical Model” (5th edition)”, which has been also cited many times in academia (the latest SCI has 993 references). In recognition of Professor Li's academic contributions, he was awarded the Fellow of the American Statistical Association in 2013. Professor Li has extensive teaching experiences and has won the Excellent Teaching Awards five times at the University of Minnesota. In his first year of service at SAIF, he won the 2019 SAIF Teaching Award.

    Professor Li received his bachelor degree in applied mathematics from Tsinghua University and his master's and doctorate degrees in statistics from the University of Waterloo.

  • Research Interests
    Industrial and Business Statistics, Experiment Design, Business Analytics and Big Data, FinTech.

    Journal Publications
    1. Zhou, Q.; Li, W.; and Xu H., Forthcoming Utilizing Individual Clear Effects for Intelligent Factor Allocations and Design Selections, Journal of Quality Technology.

    2. Bi, Xuan; Adomavicius, Gediminas; Li, William; Qu, Annie, Forthcoming Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness, Informs Journal on Computing.

    3. Chen, Kedong, William Li, and Sijian Wang, 2020, An Easy-to-Implement Hierarchical Standardization for Variable Selection under Strong Heredity Constraint, Journal of Statistical Theory and Practice.

    4. Li, William, Robert W. Mee, and Qi Zhou, 2019, Using Individual Factor Information in Fractional Factorial Designs, Technometrics.

    5. Yang, Po, and William Li, 2019, Some Properties of Foldover Designs with Column Permutations, Metrika.

    6. Errore, Anna, Bradley Jones, William Li, and Christopher J. Nachtsheim, 2017, Using Definitive Screening Designs to Identify Active First- and Second-Order Factor Effects, Journal of Quality Technology.

    7. Errore, Anna, Bradley Jones, William Li, and Christopher J. Nachtsheim, 2017, Benefits and Fast Computation of Efficient Foldover Designs, Technometrics.

    8. Li, William, and Dennis K. J. Lin, 2016, A Note on Foldover of 2n-k Designs with Column Permutations, Technometrics.

    9. Li, William, Qi Zhou, and Runchu Zhang, 2015, Effective Designs Based on Individual Word Length Patterns, Journal of Statistical Planning and Inference.

    10. Li, William, and Ji Zhu, 2014, Comments: Model Selection with Strong and Weak Heredity, Technometrics.

    11. Li, William, Christopher J. Nachtsheim, Ke Wang, Robert Reul and Mark Albrecht, 2013, Conjoint Analysis and Discrete Choice Experiments for Quality Improvement, Journal of Quality Technology.

    12. Yang, Po, and William Li, 2013, Blocked Two-level Semifoldover Designs, Journal of Statistical Planning and Inference.

    13. Tichon, Jenna G., William Li, and Robert G. Mcleod, 2012, Generalized Minimum Aberration Two-Level Split-Plot Designs, Journal of Statistical Planning and Inference.