The laws of probability, so true in general, so fallacious in particular. 
                                                      --Edward Gibbon


Yichao Wu
TransUnion Professor
 Phone: (312) 413-2156
Department of Mathematics, Statistics and Computer Science
Office: SEO 506
The University of Illinois at Chicago
E-mail: Smiley face

General Info and Research Interest

Dr. Wu received his Ph.D. in Statistics from The University of North Carolina at Chapel Hill in May 2006. He joined The University of Illinois at Chicago in August 2017. His current research interests include:
  • Statistical Machine Learning
  • Analysis of High Dimensional Big Data
  • Functional Data Analysis
  • Analysis of Sparse and Irregular Longitudinal Data
  • Nonparametric Statistics
  • Variable Selection
  • Dimension Reduction
  • Analysis of Network Data

Teaching (current semester: Fall 2021)
Selected Honors and Awards
  • Fellow, Institute of Mathematical Statistics (IMS), 2020.
  • Elected member, International Statistical Institute (ISI), 2020.
  • Fellow, American Statistical Association (ASA), 2018.
  • Leo Breiman Junior Award, Section on Statistical Learning and Data Science, ASA, 2018.
  • Faculty Early Career Development (CAREER) Award, National Science Foundation, 2011.
  • NCSU Sigma Xi Faculty Research Award, 2011
Editorial Boards

Selected Publications

  • Tucker, D. C., Wu, Y., and Müller, H.-G. (2021+) Variable selection for global Fréchet regression, Journal of the American Statistical Association, in press.
  • Wu, Y. (2021) Can't ridge regression perform variable selection? Technometrics, 63, 263-271.
  • Zheng, C. and Wu, Y. (2020). Nonparametric estimation of multivariate mixtures. Journal of the American Statistical Association, 115, 1456-1471.
  • White, K., Stefanski, L. A., and Wu, Y. (2017). Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods. Journal of the American Statistical Association, 112, 1587-1597.
  • Shin, S. J., Wu, Y., Zhang, H. H., and Liu, Y. (2017). Principal Weighted Support Vector Machines for Sufficient Dimension Reduction in Binary Classification. Biometrika, 104, 67-81.
  • Chang, J., Tang, C. Y., and Wu, Y. (2016). Local Independence Feature Screening for Nonparametric and Semiparametric Models by Marginal Empirical Likelihood. Annals of Statistics, 44, 515-539.
  • Zhang, X., Wu, Y., Wang, L. and Li, R. (2016). Variable Selection for Support Vector Machines in Moderately High Dimensions. Journal of the Royal Statistical Society, Series B, 78, 53-76.
  • Wu, Y. and Stefanski, L. A. (2015). Automatic structure recovery for additive models. Biometrika, 102, 381-395.
  • Yao, F., Lei, E. and Wu, Y.. (2015). Effective dimension reduction for sparse functional data. Biometrika, 102, 421-437.
  • Ke, T., Fan, J. and Wu, Y. (2015). Homogeneity Pursuit. Journal of the American Statistical Association, 110, 175-194.
  • Stefanski, L. A., Wu, Y., and White, K. (2014). Variable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods. Journal of the American Statistical Association, 109, 574-589.
  • Zhou, H. and Wu, Y. (2014). A Generic Path Algorithm for Regularized Statistical Estimation. Journal of the American Statistical Association, 109, 686-699.
  • Chang, J., Tang, C. Y., and Wu, Y. (2013). Marginal Empirical Likelihood and Sure Independence Feature Screening. Annals of Statistics, 41, 2123-2148.
  • Müller, H.-G., Wu, Y., and Yao, F. (2013). Continuously Additive Models for Nonlinear Functional Regression. Biometrika, 100, 607-622.
Click here for a complete list of publications.

This site was updated at February 2020