BeautyCenter Brain Image & Network Analysis                                                                Geometric & Topological Data Analysis

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. Chung received Ph.D. from the Department of Mathematics and Statistics at McGill University under Keith J. Worsley and James O. Ramsay. 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. 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, and currently working on the fourth book on topological data analysis.