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 in google  that discusses various methodological issues on brain image and network analysis, news on jobs and conferences. We also have  Brain Image Analysis seminars.


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 (persistent homology), 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 a 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

Organizing a brain imaging workshop at Seoul National University (SNU), Korea, June 20-21. 2024

Organizing a special session on  Simplicial Complex Data at ISBI, Athens, Greece, May 27-30, 2024

Received NIH grant MH133614 (PI Chung) that starts October 1, 2024. The focus will be on the geometric data analysis, manifolds data, Wasserstein distance (optimal transport), data embedding, clustering, dynamic data (time series data).

Organized TDA workshop MICCAI2021 workshop: Topological Data Analysis and its Application for Medical Data, September 18-22, 2022

Plenary talk in  Topological Data Analysis and its Application for Medical Data, September 27, 2021

Organized workshop Topological Data Analysis and Machine Learning, July 6-9, 2021

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

Received NSF MDS-2010778 grant (PI: Chung) that starts on January 1, 2021

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

Hosted ISBI 2020 Geometry workshop

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