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Runze
Li
Eberly Family Chair in Statistics; Associate Department Head
Runze Li

Biography

Runze Li is Eberly Family Chair Professor of Statistics and Associate Department Head at Penn State.

Li received his Ph.D. in Statistics from University of North Carolina at Chapel Hill in 2000.

Li's research interest includes variable selection and feature screening for high dimensional data, nonparametric modeling and semiparametric modeling and their application to social behavior science research. He is also interested in longitudinal data analysis and survival data analysis and their application to biomedical data analysis.

Li joined Penn State as an assistant professor of statistics in 2000, and became associate professor, full professor, distinguished professor and Verne M. Willaman Professor of Statistics in 2005, 2008, 2012 and 2014, respectively. Since 2018, he is the Eberly Family Chair Professor of Statistics. He received his NSF Career Award in 2004. He is a fellow of IMS, ASA and AAAS. He was co-editor of Annals of Statistics, and served as associate editor of Annals of Statistics and Statistica Sinica. He currently serves as associate editor of JASA and Journal of Multivariate Analysis.

 

Honors and Awards

  • The United Nations' World Meteorological Organization Gerbier-Mumm International Award for 2012
  • Editor of The Annals of Statistics (2013 - 2015)
  • Highly Cited Researcher in Mathematics (2014 - )
  • ICSA Distinguished Achievement Award, 2017
  • Fellow, American Association for the Advancement of Science

 

Publications

  • Liu, H., Wang, X., Yao, T., Li, R. and Ye, Y. (2019). Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming. Mathematical Programming, 78, 69-108.
     
  • Chu, W., Li, R. and Reimherr, M. (2016). Feature screening for time-varying coefficient models with ultrahigh dimensional longitudinal data. Annals of Applied Statistics, 10, 596 - 617.
     
  • Li, R., Zhong, W. and Zhu, L. (2012). Feature screening via distance correlation learning. Journal of American Statistical Association. 107, 1129 - 1139. Zou, H. and Li, R. (2008).
     
  • One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Annals of Statistics, 36, 1509-1566. Li, R. and Liang, H. (2008).
     
  • Variable selection in semiparametric regression modeling. Annals of Statistics. 36, 261-286.
     
  • Fan, J. and Li, R. (2006). Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery. Proceedings of the International Congress of Mathematicians (M. Sanz-Sole, J. Soria, J.L. Varona, J. Verdera, eds.), Vol. III, European Mathematical Society, Zurich, 595-622.
     
  • Li, R. and Sudjianto, A. (2005). Analysis of computer experiments using penalized likelihood in Gaussian kriging Models. Technometrics. 47, 111-120.
     
  • Fan, J. and Li, R. (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of American Statistical Association, 99, 710-723.
     
  • Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and it oracle properties, Journal of American Statistical Association. 96, 1348-1360.
     
  • Cai, Z., Fan, J. and Li, R. (2000). Efficient estimation and inferences for varying coefficient models. Journal of the American Statistical Association. 5, 888-902.

 

Teaching

Stat 565 - Multivariate Analysis

Stat 597 - Statistical Foundations of Data Science