BeautyCenter Brain Image Analysis &  Topological Data Analysis


Medical Science Center 4725
1300 University Ave
Madison, WI 53706

Tel: 608-217-2452

Group Emailing List

I maintain a group emailing list that discusses various methodological issues on brain image and network analysis, news on jobs and conferences.


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 and network shape variations in both normal and clinical populations using various mathematical, computational and statistical techniques. A major challenge in the field is caused by the massive amount of nonstandard high dimensional non-Euclidean imaging and network data that are difficult to analyze using available techniques. This requires new computational solutions that are formulated in a differential geometric and algebraic topological setting in addressing more complex scientific hypotheses. Other than computational neuroanatomy, my interest lies in shape analysis, network analysis, medical image analysis, functional data analysis, diffusion equations and persistent homology. Read More

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 Department of Statistics and 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. Dr. Chung’s main research area is computational neuroimaging, 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. 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 2011 for the paper that analyzed CT images. Recently he won NIH Brain Initiative Award for three years between 2017-2019 for persistent homological brain network analysis. He has written two books on brain image analysis and working on the third book that will be published in 2018 through Cambridge University Press. NIH Biosketch

What's new

>Will give a plenary talk in the 8th Annual minisymposium on computational topology, June 18-21, 2019.

>We are organizing Special session in Topological Data Analysis in KSIAM 2019

>Min-Hee Lee made the cover article in TBME. Read his paper  Lee et al. 2018.

>PhD student Yuan Wang accepted the tenure tract Assistant Professor position from University of South Carolina. She wrote  Wang et al. 2018, Annals of Applied Statistics, 12:1506-1534 (received ENAR paper award in 2014)

>Workshop on Nonstandard Brain Image Analysis (NBIA) We are organizing a two day workshop as a Satellite Meeting of OHBM between June 22-23, 2018 in the National University of Singapore.