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.