Genetic Medicine and Human Genetics
As a computational biologist and statistician by training, my research bridges statistical methodological advances and biomedical applications. My group develops computational methods and open source tools to address challenges posed by high-throughput technologies for data analysis and interpretation. Our ultimate goal is to develop methods that can integrate genomic features into the prediction of clinical outcomes, which will potentially shed new light on personalized disease diagnosis and prognosis. Currently, we focus on three main areas: developing methods that account for technical artifacts in single cell genomics data; developing methods that profile epitranscriptomics and associate epitranscriptomic variations with human health and diseases; and developing methods that characterize intra-tumor heterogeneity from cancer genomics data.