简介
Dr. Ken Chen is currently a tenured professor of Bioinformatics and Computational Biology at MD Anderson Cancer Center in Houston, Texas. He is a fellow of the American Institute of Medical and Biological Engineering (AIMBE) and a fellow of the Asia-Pacific Artificial Intelligence Association (AAIA). He received a B.Eng. degree from Tsinghua University, China (Precision Instruments, 1996), a Ph.D. from University of Illinois at Urbana-Champaign (Electrical and Computer Engineering, 2004, mentor: Mark Hasegawa-Johnson) and postdoctoral training from University of California, San Diego (Biophysics and Biochemistry, 2005). He also worked as a visiting researcher in Microsoft Research Asia (2001) and in Johns Hopkins University (Center for Language and Speech Processing, 2004). From 2005 to 2011, he worked for the Genome Institute at Washington University in St. Louis as a senior scientist and a research faculty (mentor: Elaine Mardis). Having a background in machine learning, statistical signal processing, bioinformatics, and genomics, his primary interest is to develop computational tools to analyze and interpret human genomics and clinical data towards the realization of genomic medicine. Dr. Chen has designed, developed, and co-developed a set of computational tools such as BreakDancer, novoBreak, monoVar, Monopogen, METAFlux, SiFit, TransVar, BreakTrans, BreakFusion, TIGRA, CREST, PolyScan, SomaticSniper, and VarScan, which have been widely applied to characterize individual and population genomics in various large-scale sequencing projects such as those in The Cancer Genome Atlas (TCGA), the 1000 Genomes Project and Human Cell Atlas. He is particularly interested in comprehensively and accurately constructing the genomes and the transcriptomes of various cancer and immune cell populations, towards understanding the heterogeneity and the evolution of cancer and tumor microenvironment as a consequence of genetics and treatment. He is also interested in correlating genomics with diseases towards identifying biomarkers that are useful for personalized diagnosis and prognosis.
