Ming received his Ph.D. in statistics from UCLA, and joined Peltz Lab in 2008. He has been using statistical modeling and machine learning methods for analysis of biomedical data to facilitate scientific discovery. Together with multiple other colleagues and collaborators, he has developed computational “big-data” research methods for biomedical discovery. As one example, he assembled information from > 3 trillion base pairs of whole-genome sequence data and massively parallel analysis of >8000 publicly available datasets in the Mouse Phenome Database. He then used Haplotype-Based Computational Genetic Mapping (HBCGM) method to identify genetic factors affecting many traits of biomedical interest.

Ming enjoys hiking in Yosemite and good foods.