Xiaoli Gao

Xiaoli Gao

Associate Professor

Office: Petty 130
Personal Website:
Starting year at UNCG: 2013
Office Hours: Virtual: R 11:00am-12:00pm or by appointment


Degree(s): Ph.D. in Statistics, University of Iowa (2008)


Spring 2022
  • STA-600X LEC (Experimental Course), TR 12:30-1:45, Petty Science Building 7
  • STA-667 IND (Statistical Consulting)
  • STA-703 LEC (Tpcs in High Dmnsnl Data Anlys), TR 2:00-3:15, Moore Building 331
  • STA-709 LEC (Topics in Computatnl Stats), M 6:00-8:45, Petty Science Building 130
Summer Session 1 2022
  • STA-108 LEC (Elemntry Intro Probablty Stats)


Member of the Research Group(s): Statistics
Current Students: Reetika Sarkar (Ph.D.), Matt Jester (Ph.D.)
Former Students: Bin Luo (Ph.D.)

Research Interests: High Dimensional Data Analysis, Statistical Genetics

Selected Publications

  • Gillies, C. E., Gao, X.L., Patel, N.V., Siadat, M.R., Wilson, G.D.(2012). Improved Feature Selection by Incorporating Gene Similarity into the LASSO, 2012 IEEE 12th International Conference on Data Mining Workshops. An extended version is published in International Journal of Knowledge Discovery in Bioinformatics, 3(1), 1-13, DOI: 0.4018/jkdb.2012010101.
  • Wu, Y. and Gao, X.L. (2011).Sieve estimation with bivariate interval censored data, Journal of Statistics, Application and Theory, 5, 37-61.
  • Gao, X.L. and Fang, Y.X. (2011). A note on the generalized degrees of freedom under the L1 loss function. Journal of Statistical Planning and Inference, 141, 677-686.
  • Gao, X.L. and Huang, J. (2010) A Robust Penalized Method for the Analysis of Noisy DNA Copy Number Data. BMC Genomics, 11:517.
  • Gao, X.L. and Huang, J. (2010). Asymptotic analysis of high-dimensional LAD regression with Lasso. Statistica Sinica, 20 1485-1506.

Brief Biography

Dr. Xiaoli Gao received her Ph.D. in Statistics from the University of Iowa in 2008 and joined UNCG in 2013. Her research interests include High-dimensional Data analysis, Shrinkage analysis, Statistical Genetics, Change point and Survival Analysis. More recent papers can be found on her personal webpage.