Data-Driven Predictive Modeling to Neuroimaging Data
With the advent of biomedical imaging technologies and recent advances in machine learning, it has been of great interest for analyzing neuroimaging data and investigating the underlying structural or functional characteristics in a human brain. Of various topics relative to neuroimaging data analysis, in this talk, I will introduce our recent work on machine-learning based brain disorder or disease diagnosis. A series of machine-learning methods developed in my laboratory will be discussed on structural and functional MRI data analysis. As a clinical application, due to the great importance of learned model interpretation and/or decision explanation, I will also introduce our ongoing researches in that directions and how machine learning can play roles in
Heung-Il Suk is an Associate Professor in the Department of Brain and Cognitive Engineering at Korea University. He received the BS and MS degrees in computer engineering from Pukyong National University, Busan, Korea, in 2004 and 2007, respectively, and the PhD degree in computer science and engineering, Korea University, Seoul, Republic of Korea, in 2012. From 2012 to 2014, he was a Postdoctoral Research Associate at the University of North Carolina, Chapel Hill, NC, USA. As a machine learning expert, Prof. Suk is currently focusing his researches to develop
computational methods for brain and cognitive engineering, including neuroimaging data analysis, brain-computer interface, and medical image computing. For the last 3 years, he has published more than 20 refereed top-tier journal papers. He served as a co-organizer of the 9th International Workshop on Machine Learning in Medical Imaging and is currently a member of the Editorial Board of Biomedical Research and Clinical Practice.