Dr. Kim has considerable biomedical informatics expertise in methods development for data integration using machine learning and other analytic challenges. His research entails the development and application of data integration approach to improve the ability to diagnose, treat, and prevent complex diseases. His primary focus lies in integrating multi-omics data, biological knowledge, and imaging data to better translate genomic and biomedical data from electronic health records (EHR) into clinical products. His projects have been both theoretical and applied, and they include developing novel data integration methods that combine multi-omics data and biological knowledge, predicting clinical outcomes based on interactions between multi-omic features, integrating multi-modal neuroimaging and multi-omics data, and identifying gene-gene (GxG) and gene-by-environment (GxE) interactions in several phenotypes/diseases.

He plans to continue his work in these areas, focusing primarily on providing actionable clinical products based on inter-plays within/between different dimensional genomic data. In particular, his long-term research goal is to develop and evaluate sophisticated data integration methods that simultaneously combine peoples’ individual variations in genomic (‘omic) data, imaging data, phenotype data from EHR, and environment/lifelog data for advancing precision medicine. Starting in 2016, he served as an assistant professor in the Biomedical & Translational Informatics Institute at Geisinger Health System. After that, Dr. Kim joined the University of Pennsylvania.