Enriching Statistical Inferences on Brain Connectivity for Alzheimer’s Disease Analysis
Won Hwa Kim is an Assistant Professor in the Department of Computer Science and Engineering / Graduate School of AI at POSTECH, South Korea. He obtained his Ph.D in Computer Science from University of Wisconsin – Madison in 2017, M.S. in Robotics from KAIST in 2010 and B.S. in Information and Communication Engineering from Sungkyunkwan University in 2008. Prior to joining POSTECH, he was an Assistant Professor in the Computer Science and Engineering at the University of Texas at Arlington (currently on leave-of-absence) from 2018 and also was a Researcher in Data Science team at NEC Labs., America in 2017. He is a recipient of NSF IIS CRII from National Science Foundation (NSF) in the U.S.A. and Rising STARs Award from the University of Texas System.
Machine learning has become a core component to analyze various types of data in Neuroimaging. Graph is one of the special types that comes with a set of nodes with their arbitrary connections, which differentiates itself from traditional imaging data in Euclidean spaces. In neuroimaging, data such as cortical thickness on a brain surface and brain networks are naturally represented with graphs which require sophisticated machine learning and signal processing techniques due to their irregular structure. In this talk, I will introduce our recent effort on analyzing such graph data in neuroimaging studies and report neuroscientific findings that identify disease specific variations in the brain due to Alzheimer’s Disease (AD). Our framework captures even subtle changes in the preclinical stages of AD which was not possible with conventional statistical pipelines.
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