Seungchan Kim

Seungchan Kim
Seungchan Kim, Ph.D.
Director (CCSB), Chief Scientist and Executive Professor (ECE)

Dr. Seungchan Kim is a Chief Scientist and Executive Professor at the Department of Electrical and Computer Engineering and Director of the CRI Center for Computational Systems Biology at the Prairie View A&M University (PVAMU), initiated by funding from Texas A&M University Systems‘ Chancellor’s Research Initiative (CRI) and Prairie View A&M University. Prior to this appointment, He was the Head of Biocomputing Unit and an Associate Professor at Integrated Cancer Genomics Division of Translational Genomics Research Institute (TGen). He was one of the founding faculty members of TGen, founded in 2002, by Dr. Trent, then-Scientific Director of the National Human Genome Research Institute at the National Institutes of Health. He had led computational systems biology research at the institute since 2003. He was also an Assistant Professor in the School of Computing, Informatics, Decision Systems Engineering (CIDSE) at the Arizona State University from 2004 till 2011. Dr. Kim received B.S. and M.S. degrees in Agriculture Engineering from the Seoul National University, and Ph.D. in Electrical Engineering from the Texas A&M University. He also got his post-doctoral training at the Cancer Genetics Branch of National Human Genome Research Institute.

Dr. Kim is well recognized in the field of Bioinformatics and Computational Systems Biology research, both nationally and internationally, with more than 70 peer-reviewed articles with more than 5,000 citations. His research interests include: 1) mathematical modeling of genetic regulatory networks, 2) development of computational methods to analyze multitude of high throughput multi-omics data to identify disease biomarkers, and 3) computational models to diagnose patients or predict patient outcomes, for example, disease subtypes or drug response. His studies have had a large influence on the development of computational tools to study underlying mechanisms for cancer development and better understand the molecular mechanisms behind cancer biology and biological systems.

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