My research interest is to understand how galaxies and their constituents (gas and stars) assemble and evolve with the cosmic time. I focus on applying modern statistical and machine learning methods to analyze and interpret multidimensional data of galaxies. Currently, I am modeling multi-variate spatial patterns and inter-dependencies of dust/gas, metallicity, and star formation as well as morphological substructures of nearby galaxies. Recently, I developed a cost-effective method that uses dust absorption to estimate molecular gas masses of galaxies. I have applied this method to study gas in active galactic nuclei (AGNs) and post-starburst galaxies.