Sean is a postodoctoral fellow in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health, advised by Prof. Stephanie Hicks. He recently earned his Ph.D. in Computational Biology from the Department of Biomedical Engineering at Oregon Health & Science University, advised by Prof. Abhinav Nellore. As part of his dissertation research, he published the recountmethylation Bioconductor package, the recountmethylation_instance Snakemake workflow, and affiliated uniformly processed compilations of tens of thousands of public DNA methylation array data mined from the Gene Expression Omnibus.
In addition to his education and research, Sean possesses 8 years of experience programming in R and 5 years of experience programming in Python to conduct data science with high-dimensional data. He is highly capable and experienced at applying computer programming to analyze genomics and epigenomics data, including large-scale analyses of public DNA methylation array data, quantification of alternative splicing detected from short-read and long-read RNA-seq data, investigations of biological aging in gastroesophageal cancer and cancer precursor tissues, and more. Sean is passionate about reproducible research, open-source programming, and data-driven applications for machine learning. He is actively pursuing machine learning applications to mine, harmonize, and analyze public omics data at scale.