Department of Statistics

University of Illinois at Urbana-Champaign

725 S. Wright Street

Champaign, IL 61820 USA

Office: Illini Hall, 104F

Phone: (217) 333-6017

Fax: (217) 244-7190

Email: liangf at illinois dot edu

**Education**

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

**Publications** [Google Scholar]

**Patents**

- U.S. Patent 6,990,486: “Systems and methods for discovering fully dependent patterns.” Sheng Ma, Joseph L. Hellerstein, and Feng Liang. January 24, 2006

**Grants**

- (PI) “Learning Dependence Structures with Bayesian Regularization,” NSF DMS 1916472, 2019 – 2022
- (Co-PI) “Bayesian Estimation of Restricted Latent Class Models,” NSF SES 1758631, 2018 – 2020.
- (PI) “Bayesian Learning with Structured Sparsity,” NSF DMS 1209152, 2012 – 2015.
- (PI) “Bayesian Methods for Multitask Learning,” UIUC Research Board 12112, 2011 – 2013.
- (PI) “Probabilistic Models and Geometry for High Dimensional Data,” NSF DMS 0732276, 2007 – 2011.
- (Co-PI) “A Virtual Center to Promote Collaboration between US- and China-based Re-searchers in Statistical Science,” NSF DMS 0630950, 2006 – 2009.
- (PI) “Bayesian Inference with Overcomplete Wavelet Dictionary,” Duke Arts and Sciences Research Council, 2006 – 2007.
- (Co-PI) “High Dimensional Model Averaging and Model Selection,” NSF DMS 0406115, 2004 – 2007.

**Professional Activities**

- Associate Editor, Bayesian Analysis, 2009 – 2016.
- ISBA Mitchel Prize Committee, 2009-2010.
- Program Committee Member for AIStats, 2009 & 2013.
- Senior Program Committee Member for AIStats, 2011.
- Program Committee Member for Midwest Statistics Research Colloquium, 2011 & 2013
- ISBA Bulletin Editor, 2013 – 2015.
- Program Chair, ASA Section on Statistical Computing, 2014.
- ISBA Savage Award Committee, 2017-2018.
- ISBA Board of Directors, 2017 – 2019.
- ISBA Executive Secretary, 2019 -2021.

**Teaching**

- 542: Statistical Learning
- 578: Bayesian Nonparametrics
- 578: Bayesian Machine Learning
- 575: Large Sample Theory
- 510: Mathematical Statistics
- 410: Statistics and Probability II
- 425: Applied Regression and Design
- 424: Analysis of Variance
- CS 598: Practical Statistical Learning (Online)
- Probability and Statistics in Engineering (Duke)
- Data Mining and Machine Learning (Duke)
- Statistical Decision Theory (Duke)