Dr. William Li is a Professor of Management at the Shanghai Advanced Institute of Finance (SAIF), Shanghai Jiao Tong University. Prior to joining SAIF in 2018, Professor Li was a tenured full professor and held the Eric Jing Professorship at the Carlson School of Management of the University of Minnesota. He has also served as an invited professor at the School of Management at Fudan University.
Professor Li's research areas encompass industrial and business statistics, experimental design, business analysis and big data, and financial technology. He has published nearly 40 high-level academic papers in leading journals in the fields of both business and applied statistics (particularly in top journals recognized for experimental design), such as INFORMS Journal on Computing, Journal of Operations Management, Journal of the American Statistical Association, and Technometrics. Additionally, he is a co-author of the widely used statistics textbook Applying Linear Statistical Model (5th edition), which has been extensively cited in academia.
In recognition of his academic contributions, Professor Li was awarded the prestigious Fellow of the American Statistical Association in 2013.
Professor Li has extensive teaching experience and has received numerous accolades for his exceptional teaching abilities. He has won the Excellent Teaching Award five times at the University of Minnesota. At SAIF, he received the SAIF Teaching Award in 2019 and the Shanghai Jiao Tong University Excellent Educator Award in 2021.
Journal Publications
1. Chen, K., Linderman, K., Su, H.C., and Li., W., 2024, Last-Minute Coordination: Adapting to Demand to Support Last-Mile Operations, Journal of Operations Management.
2. He, L., Li, W., Song, D., and Yang, M., 2022, A Systematic View of Information-Based Optimal Subdata Selection: Algorithm Development, Performance Evaluation, and Application in Financial Data, Statistica Sinica.
3. Zhou, Qi, William Li, and Hongquan Xu, 2023, Utilizing Individual Clear Effects for Intelligent Factor Allocations and Design Selections, Journal of Quality Technology.
4. Chen, Ping-Yang, Ray-Bing Chen, Jui-Pin Li, and William Li, 2022, Particle Swarm Exchange Algorithms with Applications in Generating Optimal Model-Discrimination Designs, Quality Engineering.
5. Bi, Xuan, Gediminas Adomavicius, William Li, and Annie Qu, 2022, Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness, Informs Journal on Computing.
6. 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.
7. Li, William, Robert W. Mee, and Qi Zhou, 2019, Using Individual Factor Information in Fractional Factorial Designs, Technometrics.
8. Yang, Po, and William Li, 2019, Some Properties of Foldover Designs with Column Permutations, Metrika.
9. 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.
10. Errore, Anna, Bradley Jones, William Li, and Christopher J. Nachtsheim, 2017, Benefits and Fast Computation of Efficient Foldover Designs, Technometrics.
11. Li, William, and Dennis K. J. Lin, 2016, A Note on Foldover of 2n-k Designs with Column Permutations, Technometrics.
12. Li, William, Qi Zhou, and Runchu Zhang, 2015, Effective Designs Based on Individual Word Length Patterns, Journal of Statistical Planning and Inference.
13. Li, William, and Ji Zhu, 2014, Comments: Model Selection with Strong and Weak Heredity, Technometrics.
14. Yang, Po, and William Li, 2014, Blocked Two-level Semifoldover Designs, Journal of Statistical Planning and Inference.
15. 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.
16. Tichon, Jenna G., William Li, and Robert G. Mcleod, 2012, Generalized Minimum Aberration Two-Level Split-Plot Designs, Journal of Statistical Planning and Inference.
