Hyun Min Kang, PhD is currently an Associate Professor of Biostatistics in the University of Michigan School of Public Health. Dr. Kang received his Bachelor’s and Master’s degrees in Electrical Engineering at Seoul National University in 1998 and 2000, respectively. After finishing the military service, he became fascinated in genome science while working as a research fellow at Genome Research Center for Diabetes and Endocrine disease in the Seoul National University Hospital until 2003. He received his PhD degree in Computer Science from University of California, San Diego, capitalizing on computational/statistical genetics. He joined in the Biostatistics Department of the University of Michigan School of Public Health in 2009, started his tenure-track appointment in 2011, and was granted tenure in 2017.

Dr. Kang’s research focuses on statistical and computational methods for population-scale, genome-wide analysis of large-scale genomic, transcriptomic, and epigenomic data collected by high-throughput assays. During his PhD training, he focused on methods for accurate genome-wide and transcriptome-wide analysis of inbred mouse strains. In 2007, he produced genome-wide local ancestry map of inbred mice from >8M inbred mouse single nucleotide variants. In 2008, he developed EMMA (Efficient Mixed Model Association), a statistical method that enabled rapid genome-wide association analysis (GWAS) of inbred mice while accounting for population structure and cryptic relatedness. His work has been cited >1,000 times to date, and it remains the 3rdmostcitedpaperinthejournalGenetics in the past decade. In 2010, he published EMMAX, a rapid extension of EMMA scaled to human GWAS. This work, which received >1,100 citations to date, motivated development of many novel GWAS methods capitalizing on linear mixed models. He also published many highly cited methods for robust analysis of gene expression datasets and for genotype imputation.

At the University of Michigan, Dr. Kang focused on developing methods for accurately and efficiently analyzing DNA sequence reads at population scale. He has played leading roles in large-scale genome sequencing studies, including the 1000 Genomes Project, Exome Sequencing Project, type 2 diabetes sequencing project (T2D-GENES). Currently, he is producing joint variant call sets across >130,000 genomes deeply sequenced for NHLBI TOPMed (Trans-Omics Precision Medicine) project. He also authored many popular software tools, such as verifyBamID, GotCloud, vt, and EPACTS, for sequence-based GWAS. Recently, he is developed novel computational tool demuxlet and experimental workflow mux-seq to enable innovative and cost-effective single-cell transcriptomic profiling in population scale. He has published more than 91 peer-reviewed papers, including 34 papers with >100 citations, with >28,000 citations in total. He is a Principal Investigator of multiple NIH-funded research projects (U01, R21), private- and university-sponsored projects.

Dr. Kang hopes to benefit communities of genome scientists, molecular biologists, cell biologists, and medical geneticists by developing practical, accurate, robust, and scalable methods for analyzing big genomic data. In the next 10 years, he is particularly interested in integrating genetic variants with single cell transcriptomic and epigenomic data to precisely unravel the etiology of complex traits and its underlying molecular mechanisms.