Medical Science Center 4750
1300 Highland Ave
Madison, WI 53706
Tel: 608-217-2452
Email:mkchung@wisc.edu
Research Interest
My main
research area is computational neuroanatomy, where non
invasive brain imaging modalities such as magnetic
resonance imaging (MRI) and diffusion tensor imaging
(DTI) are used to map spatiotemporal dynamics of the
human brain. Computational neuroanatomy deals with the
computational problems arising from the quantification
of the structure and the function of the human brain. My
research has been concentrated on the methodological
development of quantifying anatomical shape variations
in both normal and clinical populations using various
mathematical and statistical techniques. A major challenge in the field is caused
by the massive amount of nonstandard high dimensional
non-Euclidean imaging data that are difficult to analyze
using available techniques. This requires new
computational solutions that are formulated
in a differential geometric
setting in addressing more complex scientific
hypotheses. Other than computational neuroanatomy, my
interest lies in shape analysis, medical image analysis,
nonparametric regression, functional data analysis,
random fields theory, and partial differential
equations. If you are
interested in working with me as a graduate student or a
postdoc, please contact me.
Short Bio
Moo K. Chung,
Ph.D. is an Associate Professor in the Department of
Statistics, Biostatistics and Medical Informatics at
the University of Wisconsin-Madison. He is also
affiliated with the Waisman Laboratory for Brain
Imaging and Behavior. Dr. Chung received Ph.D. in
Statistics from McGill University under Keith J.
Worsley and James O. Ramsay in 2001. Dr. Chung’s
main research area is computational neuroanatomy,
where noninvasive brain imaging modalities such as
magnetic resonance imaging (MRI) and diffusion
tensor imaging (DTI) are used to map the
spatiotemporal dynamics of the human brain. His
research concentrates on the methodological
development required for quantifying and contrasting
anatomical shape variations in both normal and
clinical populations at the macroscopic level using
various mathematical, statistical and computational
techniques. Recently, Dr. Chung won Vilas Associate
Award for years 2013-2014 for his applied
topological research (persistent homology) to
medical imaging and the Editor's Award for best
paper published in Journal of Speech, Language, and
Hearing Research in year 2010 for the paper that
analyzed vocal tract CT images.
