BeautyCenter Brain Image Analysis &  Topological Data Analysis


Medical Science Center 4725
1300 University Ave
Madison, WI 53706

Tel: 608-217-2452


My main research area is computational neuroimaging, where non invasive brain imaging modalities such as magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and functional-MRI are used to map spatiotemporal dynamics of the human brain. Computational neuroimaging 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 and functional differences 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 neuroimaging, my interest lies in topological data analysis, shape analysis, network analysis, image analysis, functional data analysis, diffusion equations and persistent homology. Read more on NIH funded project

Short Bio.

Moo K. Chung, Ph.D. is an Associate Professor of 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. from the Department of Mathematics at 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 between 2017-2020 for large-scale persistent homological brain network analysis. He has written three books on brain image and network analysis.

What's new

Giving a plenary talk in the MICCAI2021 workshop: Topological Data Analysis and its Application for Medical Data, September 27, 2021

We are organizing workshop on Topological Data Analysis and Machine Learning, July 6-9, 2021

We are hosting the Satellite Meeting for OHBM2021: Workshop on Nonstandard Brain Image Analysis (NBIA). June 17-18, 2021

We received NSF MDS-2010778 Collaborative Computational Neurosciences grant (PI: Chung) that starts on January 1, 2021

We received NIH R01 EB028753 grant (PI: Chung) that started May 1, 2020

We hosted ISBI 2020 Geometry workshop

Plenary talk in the 8th Annual minisymposium on computational topology, June 18-21, 2019.

Special session in Topological Data Analysis in KSIAM 2019

Min-Hee Lee made the cover article in TBME:  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 (the paper is based on the ENAR paper award in 2014)