- Ph.D. University of Georgia
- M.S. University of Central Florida
- B.S. University of Texas at Austin
Dr. Wenbo Wu is an assistant professor in management science and statistics at The University of Texas at San Antonio. Before moving to San Antonio, he was an assistant professor at the University of Oregon. Wu received his Ph.D. in statistics from the University of Georgia in 2015.
His active research areas include high-dimensional data modeling and inference, dimension reduction, variable selection and causal inference. Wu also collaborates with researchers in other domains such as finance, marketing, engineering and computer science.
Wu has established a good publication record in high-ranking journals and teaches both undergraduate and graduate level statistics and data analytics courses. He is a four-time winner (as graduate student and faculty adviser) of the SAS Data Analytics Shootout competition, which is a national analytical competition. Wu also worked as a lead consultant at the Statistical Consulting Center at the University of Georgia and brings industry experience from his work with organizations.
- Sufficient dimension reduction
- High-dimensional modeling
- Feature selection
- Causal inference
- Machine learning
- “A Novel Mobility-Based Approach to Derive Urban-Scale Building Occupant Profiles and Analyze Impacts on Building Energy Consumption,” with B. Dong, Q. Wang, M. Kong, D. Yan, J. An and Y. Liu, Applied Energy, Vol. 278, No. 15, 2020.
- “Pseudo Estimation and Variable Selection in Regression,” with X. Yin, Journal of Statistical Planning and Inference, Vol. 208, 2020, pp. 25-35.
- “Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction,” with W. Luo and Y. Zhu, Journal of Causal Inference, Vol. 7, No. 1, 2019.
- “Partial Projective Resampling Method for Dimension Reduction: With Applications to Partially Linear Models,” with H. Hilafu, Computational Statistics and Data Analysis, Vol. 109, 2017, pp. 1-14.
- “Stable Estimation in Dimension Reduction,” with X. Yin, Journal of Computational and Graphical Statistics, Vol. 24, No. 1, 2015, pp. 104-120.