Curriculum Vitae

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

Academic Positions

  • 2002 – 2008: Assistant Professor, Institute of Statistics and Decision Sciences, Duke University
  • 2006 – 2011: Assistant Professor, Department of Statistics, UIUC
  • 2011 – present: Associate Professor, Department of Statistics, UIUC

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)