Lab Members

John D. Storey

John is the William R. Harman ‘63 and Mary-Love Harman Professor in Genomics, primarly appointed in the Lewis-Sigler Institute for Integrative Genomics at Princeton University. He has associated faculty appointments in Applied and Computational Mathematics, the Center for Statistics and Machine Learning, Computer Science, Molecular Biology, Operations Research and Financial Engineering, and the Princeton Institute for Computational Science and Engineering (PICSciE). He is the director (with Josh Akey) of the NHGRI Quantitative and Computational Biology Graduate Training Program at Princeton University. For more information, check out a brief biography, his personal website, and his GitHub, Google Scholar, and LinkedIn profiles.

Danfeng Chen

Danfeng is a PhD student in Quantitative and Computational Biology at Princeton University. She received her BS in Biological Sciences from Fudan University and her MS in Computational Biology and Quantitative Genetics from Harvard University.

Danfeng’s research is focused on new theory and methods for heritability in genome-wide association studies, new estimation methods for coancestry in structured populations, and developing surrogate variable analysis for sequencing-based molecular profiling, such as RNA-seq. For more information, check out Danfeng’s GitHub profile.

Riley Skeen-Gaar

Riley is a PhD student in Quantitative and Computational Biology at Princeton University. He has a BA from University of Colorado Boulder, double-majoring in Biochemistry and Molecular, Cellular, and Developmental Biology. Although Riley started as a wet lab biologist as an undergraduate, he has since developed an expertise in statistical genomics while a PhD student.

Riley is developing likelihood and empirical Bayes based methods for surrogate variable analysis. He is especially interested in applications to heteroskedastic data, such as that encountered in RNA-seq and other sequencing-based molecular profiling studies. For more information, check out Riley’s GitHub and LinkedIn profiles.

Yushi Tang

Yushi is a PhD student in Quantitative and Computational Biology at Princeton University. He received his BS from Peking University, double-majoring in Environmental Statistics and Economics, and MS in Computational Biology and Quantitative Genetics from Harvard University.

Yushi is working primarily in two areas. The first is in understanding the role of ancestral population as a conditional variable when performing genome-wide association studies and heritability estimation. The second is developing theory and methods for causal inference in population-based genome-wide genetics studies. For more information, check out his personal website and his GitHub, Google Scholar, and LinkedIn profiles.

Lab Alumni