I am currently a UW Data Science Postdoctoral Fellow under the supervision of Prof. Bill Noble at the University of Washington. My postdoctoral work focuses on developing machine learning methods for interpreting high-throughput genomic data sets of 3D genome architecture, with a particular emphasis on embryonic development in mouse. Currently I am working on the pseudotime analysis and integration of imaging and sequencing data.
I got my Ph.D. in interdisciplinary statistics and operations research (INSTORE) with a machine learning concentration at UNC at Chapel Hill in 2021. I was fortunate to be advised by both Prof. Jan Hannig and Prof. Yun Li. I obtained my M.S. in Statistics at UNC at Chapel Hill in 2017 and my B.S. in mathematics and applied mathematics at Beijing University of Posts & Telecommunications in 2016.
Integration of imaging and sequencing data
Single-cell RNA sequencing
Single-cell ATAC sequencing analyses
Allelic Expression Analysis
Genome-wide association studies
Bulk deconvolution, cell-type-specific analyses
Generalized fiducial inference
Deep fiducial inference and approximate fiducial computation
First-order approximate fiducial inference
HiC, HiChIP/PLAC-Seq chromatin 3D organization data