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Feng Liang

Profile picture for Feng Liang

Contact Information

113 D Illini Hall
725 S. Wright St.
M/C 374
Champaign, IL 61820
Professor

Biography

Professor of Statistics

University of Illinois Urbana-Champaign

 

Research Interests

Bayesian methods
Decision theory
Information theory
Minimum description length principle
Data mining

Education

Ph.D. Statistics, Yale University, New Haven, CT, 2002
B.A. Mathematics, Peking University, P.R. China, 1997

Awards and Honors

Associate Editor for Bayesian Analysis, 2009 – 2015
Editor for the ISBA Bulletin, 2013 – 2015

Additional Campus Affiliations

Professor, Statistics
Interim Director, Program in Actuarial Science and Risk Management, College of Liberal Arts and Sciences
Professor, Information Trust Institute
Professor, Biomedical and Translational Sciences

Honors & Awards

Associate Editor for Bayesian Analysis, 2009 – 2015
Editor for the ISBA Bulletin, 2013 – 2015

Recent Publications

Qian, C., Ganter, T., Michalenko, J., Liang, F., & Adams, J. (2024). Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain. Statistical Analysis and Data Mining, 17(5), Article e11709. https://doi.org/10.1002/sam.11709

Chen, Y., He, S., Yang, Y., & Liang, F. (2023). Learning Topic Models: Identifiability and Finite-Sample Analysis. Journal of the American Statistical Association, 118(544), 2860-2875. https://doi.org/10.1080/01621459.2022.2089574

Gan, L., Narisetty, N. N., & Liang, F. (2022). BAYESIAN ESTIMATION OF GAUSSIAN CONDITIONAL RANDOM FIELDS. Statistica Sinica, 32(1), 131-152. https://doi.org/10.5705/ss.202020.0118

Wang, J., Ouyang, Y., Ji, Y., & Liang, F. (2022). An Ensemble EM Algorithm for Bayesian Variable Selection. Bayesian Analysis, 17(3), 879-900. https://doi.org/10.1214/21-BA1275

Yin, W., Zhao, S. D., & Liang, F. (2022). Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models. Lifetime Data Analysis, 28(2), 282-318. https://doi.org/10.1007/s10985-022-09549-5

View all publications on Illinois Experts