Event Location: NSH 3305
Bio: Professor Vidal received his Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2003. He has been on the faculty of the Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University since 2004, where he currently is an Associate Professor. His research interest are biomedical image analysis, computer vision, machine learning, hybrid systems, robotics and signal processing. Dr. Vidal has received numerous awards for his work, including the IAPR 2012 J.K. Aggarwal Prize for “outstanding contributions to generalized principal component analysis (GPCA) and subspace clustering in computer vision and pattern recognition”, the 2012 Best Paper Award in Medical Robotics and Computer Assisted Interventions, the 2012 Best Paper Award at the Conference on Decision and Control, the 2009 ONR Young Investigator Award, the 2009 Sloan Research Fellowship, the 2005 NFS CAREER Award and the 2004 Best Paper Award Honorable Mention at the European Conference on Computer Vision. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, the SIAM Journal on Imaging Sciences and the Journal of Mathematical Imaging and Vision, and has served as an area chair or program committee member for all major conferences in computer vision and medical imaging. He is a senior member of the IEEE and a member of the ACM.

Abstract: In the era of data deluge, the development of methods for discovering structure in high-dimensional data is becoming increasingly important. Traditional approaches often assume that the data is sampled from a single low-dimensional manifold. However, in many applications in signal/image processing, machine learning and computer vision, data in multiple classes lie in multiple low-dimensional subspaces of a high-dimensional ambient space. In this talk, I will present methods from algebraic geometry, sparse representation theory and rank minimization for clustering and classification of data in multiple low-dimensional subspaces. I will show how these methods can be extended to handle noise, outliers as well as missing data. I will also present applications of these methods to video segmentation and face clustering.