Yufeng Liu, PhD

About

Dr. Yufeng Liu’s research is focused on developing statistical/computational methods and tools with applications to complex and high dimensional data. In particular, he is interested in statistical machine learning methods and theory. His previous and current works include statistical machine-learning methods for classification, regression, graphical models, clustering and variable selection as well as various topics related to The Cancer Genome Atlas (TCGA) data and imaging. He is a member of the Cancer Genetics Program at the UNC Lineberger Comprehensive Cancer Center.

Honors and Awards

Ruth and Phillip Hettleman Prize for Artistic and Scholarly Achievement
2010, University of North Carolina at Chapel Hill

Faculty Early Career Development (CAREER) Award
2008–2013, National Science Foundation

Junior Faculty Development Award
2006, University of North Carolina at Chapel Hill

Ransom and Marian Whitney Research Award, Statistics
2004, The Ohio State University

Student Paper Competition Winner
2003, American Statistical Association’s Sections on Statistical Computing and Graphics

Distinguished University Fellowship
1999 & 2004, The Ohio State University

Teaching Interests

Dr. Liu teaches a variety of classes in the Department of Statistics and Operations Research.

Research Activities

High dimensional data analysis
Machine learning
Nonparametric statistics
Bioinformatics
Cancer genomics

Service Activities

Associate Editor (10/2011 – present), Statistica Sinica

Guest Editor (2011 – 2012), A Special Issue for Statistical and Its Interface

Associate Editor (1/2008 – 9/2011), Journal of the American Statistical Association (Theory and Methods)

Program Chair-Elect, Program Chair, Section on Statistical Learning and Data Mining of ASA, 2012-2013

Best paper award committee member for Journal of Nonparametric Statistics, 2009- 2010

Local Scientific Coordinator for the Program on Statistical and Computational Methodology for Massive Datasets at SAMSI, NC, 2012 – 2013

Faculty fellow on the Program of High Dimensional Inference and Random Matrices at SAMSI, NC, 2006 – 2007

Key Publications

Soft or hard classification? Large margin unified machines. Liu, Y., Zhang, H.H., Wu, Y. (2011). Journal of the American Statistical Association, 106.

Adaptive weighted learning for unbalanced multicategory classification. Qiao, X. and Liu, Y. (2009). Biometrics, 65.

Probability estimation for large margin classifiers. Wang, J., Shen, X., and Liu, Y. (2008). Biometrika, 95.

Quantile regression in Reproducing Kernel Hilbert Spaces. Li, Y., Liu, Y., and Zhu, J. (2007). Journal of the American Statistical Association, 102.

Multicategory psi-learning. Liu, Y. and Shen, X. (2006). Journal of the American Statistical Association, 101.

Education