B.S., 2005, biology, Fudan University, China
Ph.D., 2010, genetics and molecular biology, University of North Carolina at Chapel Hill
Ph.D., 2015, statistics, University of North Carolina at Chapel Hill
Yuying Xie’s research mainly focuses on the area of statistical machine learning and consists of two themes: the development of new statistical procedure for high dimensional network models using complex and noisy datasets, and the inference of association or causal relations among genes, external phenotypes, and intermediate phenotypes, such as gene expression levels and biomarker concentrations.
Xie obtained his B.S. in biology from Fudan University, China. He then attended the University of North Carolina at Chapel Hill, where he received his first Ph.D. in genetics under Professor David Threadgill and his second Ph.D. in statistics under Professors Yufeng Liu and William Valdar. During his statistics Ph.D., he was also a graduate fellow at the Statistical and Applied Mathematical Sciences Institute.
• Statistical Machine Learning
• High Dimensional Data Anaylsis
• Graphical Models
• Causal Inference
• Measurement Error
• QTL/eQTL Mapping
Dependent Gaussian Graphical Models with Application to Mouse Genetics.
Biometrika,103, 493-511(An earlier version won the ENAR 2014 student paper
FS-17: STT 873 Statistical Learning and Data Mining
SS-18: CMSE 820 Math Foundations of Data Science
Click "Teaching" link to see past courses.