The UMBC CSEE Seminar Series Presents
Graphical-model-based machine learning for neuroimaging data
Professor Rong Chen
University of Maryland School of Medicine
12noon-1pm Friday, 30 October 2015, ITE 102, UMBC
Two important problem in neuroimaging data mining is high-dimensionality and temporal network modeling. Analyzing high-dimensional neuroimaging data is a very challenging problem. We developed an algorithms called Graphical-Model-based Multivariate Analysis (GAMMA) to model complex interactions among brain regions and a clinical variable. GAMMA has embedded dimension reduction and regularization mechanism. GAMMA has been used in distinguishing patients with mild cognitive impairment and normal elderly.
Identifying spatial-temporal interactions among brain regions from longitudinal structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We developed a dynamic Bayesian network based method called structural dynamic network analysis (SDNA) to solve this problem. SDNA enables the detection of spatial-temporal interactions among brain regions, leading to dynamic network analysis. SDNA has been used to model trajectory changes in patients with Alzheimer’s disease.
Dr. Rong Chen is an Assistant Professor at in the department of Radiology the University of Maryland School of Medicine. He completed his Ph.D. in Electrical and Computer Engineering at Washington State University in 2003, and his MTR in Translational Medicine at the University of Pennsylvania in 2012. He published 45 peer-reviewed research articles in the areas of neuroimaging and data mining. His research interests include computational neuroscience, data mining, medical image analysis, and translational medicine.
Hosts: Professors Fow-Sen Choa () and Alan T. Sherman ()
About the CSEE Seminar Series: The UMBC Department of Computer Science and Electrical Engineering presents technical talks on current significant research projects of broad interest to the Department and the research community. Each talk is free and open to the public. We welcome your feedback and suggestions for future talks.