New Members

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Xuanyao Liu
Postdoctoral Fellow

XuanyaoXuanyao obtained her Ph.D. from National University of Singapore and joined the Price Lab in December 2014. She worked on population genetics and developed statistical methods for detecting positive natural selection signals in both human and parasite genome. Her current research focuses on partitioning heritability across functional categories and inferring genetic component of population differences in phenotypes.



Wonil Chung
Postdoctoral Fellow

Dr. Wonil Chung received his Ph.D. in Biostatistics from the University of North Carolinawonil chung2 at Chapel Hill (UNC-CH) and joined the Liang lab as a Postdoctoral Fellow in June 2014. His research mainly focuses on the development of statistical methodologies for genome-wide association studies (GWAS) on complex human diseases and implementation of efficient software for application to large scale genomic and epigenomic data. He will work on the statistical analysis of large genetic data and actively participate in many projects during his years at HSPH.



Ga Liao
Visiting Research Fellow

Dr. Ga Liao is a Visiting Research Fellow from the West China School of Stomatology, ga liaoSichuan University, P.R. China. During his year in Boston, he will be working with Dr. Liming Liang in the Epidemiology Department at HSPH. Dr. Liao’s work explores the susceptibility variants and genes related to oral diseases, especially for head and neck cancers via whole genome sequencing and GWAS data. He will also study the underlying mechanisms of these causative genes.





Hilary FinucaneHilary Finucane
Doctoral Student

Hilary Finucane is a Doctoral student in the Mathematics Department at the Massachusetts Institute of Technology.  Her research interests are probability and statistics with applications in biology.  She joined the Price Lab as a Summer rotation student from May to July 2013 and became a full-time member of the Price lab in 2014.  While at HSPH, Hilary will work on the project of estimating genetic correlations between pairs of traits (including quantitative and/or disease traits) from summary statistic data.  Despite important progress on estimating genetic correlations from raw genotypes, this has never previously been done using only summary statistic data.