Ming-Hui Chen
President
(2018 – 2020)
Ming-Hui Chen, Ph.D., is Professor and
Head of the Department of Statistics at the
University of Connecticut. He received his
Ph.D. in Statistics from Purdue University in
1993. He was elected to Fellow of
International Society for Bayesian Analysis
in 2016, Fellow of the Institute of
Mathematical Statistics in 2007, Fellow of
American Statistical Association in 2005, and
an elected ordinary member of the
International Statistical Institute (ISI) in
1999. He has been awarded several US NIH and
NSF grants since 1997. He received the AAUP
(American Association of the University
Professors) Research Excellence Award and the
CLAS (College of Liberal Arts and Sciences)
Excellence in Research Award in the Physical
Sciences Division, University of Connecticut,
in 2013 and the UCONN Alumni Association's
University Award for Faculty Excellence in
Research and Creativity (Sciences) in 2014.
He has supervised over 20 PhD students since
2001. He was the President (2013) of the
International Chinese Statistical Association
(ICSA) and served on the board of directors
of the International Society for Bayesian
Analysis (ISBA) for 2011-2013.
He co-authored/co-edited five books and he
has also published over 365 articles in
mainstream statistics, biostatistics, and
medical journals. His article entitled "Monte
Carlo Estimation of Bayesian Credible and HPD
Intervals" with Q.-M. Shao, published in
Journal of Computational and Graphical
Statistics (1999, Volume 8, pages 69-92), is
a widely-used method for computing highest
posterior density (HPD) intervals in Bayesian
estimation. It becomes a standard default
method for computing HPD intervals. This
method has been implemented in SAS and it is
also cited in the FDA guidance, "Guidance for
the Use of Bayesian Statistic Medical Device
Clinical Trials" (February 5,
2010). Currently, he serves as Editors for
Bayesian Analysis and Statistics and Its
Interface as well as Associate Editors for
Journal of the American Statistical
Association, Lifetime Data Analysis, and
Journal of Computational and Graphical
Statistics.
