Our lab is interested in developing general machine learning approaches for integrative analysis of large-scale genomic data to understand the molecular characteristics of biological functions and phenotypes. We design theoretically principled methods in the categories of kernel methods, graph-based learning algorithms, sequence alignment methods and various statistical models for a unified analysis of the biological data in a data-driven perspective. Our current projects center around the following topics,