Our research focuses on the development of statistical methods and computational tools for analyzing large-scale genomics and proteomics data to "contribute to prevent and cure of cancer".
We are particularly interested in 1) developing bioinformatics approach to incorporate multiple data sources, select biologically relevant markers, and predict clinical outcomes in an integrated manner, 2) devising novel statistics to quantify multi-scale and nonlinear correlation, which cannot be detected using traditional linear methods, and 3) developing visual analytics for analyzing multi-modality data from single cell to bulk samples to understand tumor heterogeneity and tumor immune interactions to enhance therapeutic strategies for cancer patients