Statistics Faculty

Feng Liang

  • Associate Professor
  • Department of Statistics
  • University of Illinois at Urbana-Champaign
  • 104F Illini Hall, MC 374
  • 725 S. Wright St.
  • Champaign, IL 61820 USA
  •  
  • (217) 333-6017
  • liangf@illinois.edu
  • Website
  • Educational Background

  • PhD, Statistics, Yale University, 2002
  • Research Interests

  • Bayesian methods
  • Decision theory
  • Information theory
  • Minimum description length principle
  • Data mining
  • Selected Publications

    B. Li, F. Liang, J. Hu, and X. He (2012) Reno: Regularized Nonparametric Analysis of Protein Lysate Array Data. Bioinformatics. In press.

    E. I. George, F. Liang and X. Xu (2012) From Minimax Shrinkage Estimation to Minimax Shrinkage Prediction. Statistical Science 27: 82-94.

    F. Liang (2012) Comment on Article by Sancetta. Bayesian Analysis 7:45-46.

    Y. Yang, X. Chu, F. Liang, and T. Huang (2012) Pairwise Exemplar Clustering. In Proceedings of the 26th Conference on Artificial Intelligence, Toronto, Canada.

    J. Xu and F. Liang (2010). Bayesian co-segmentation of multiple MR images. Statistics and Its Interface, 3 513-521.

    X. Xu and F. Liang (2010). Asymptotic minimax risk of predictive density estimation for nonparametric regression. Bernoulli, 16(2) 543-560.

    Gao J, Liang F, Fan W, Wang C, Sun Y, and Han J. (2010). On community outliers and their efficient detection in information networks. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington, DC.

    J. Chu, M. Clyde, and F. Liang (2009). Bayesian function estimation using continuous wavelet dictionaries. Statistica Sinica, 19 1419-1438.

    J. Gao, F. Liang, W. Fan, Y. Sun, and J. Han (2009). Bipartite Graph-based Consensus Maximization among Supervised and Unsupervised Models. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada.

    F. Liang, R. Paulo, G. Molina, M. Clyde, and J. Berger (2008). Mixtures of g-priors for Bayesian variable selection. J. Amer. Statist. Assoc., 103 410-423.