Deva Ramanan is an associate professor of Computer Science at the University of California at Irvine. Prior to joining UCI, he was a Research Assistant Professor at the Toyota Technological Institute at Chicago. He received his B.S. in computer engineering from the University of Delaware in 2000, graduating summa cum laude. He received his Ph.D. in Electrical Engineering and Computer Science from UC Berkeley in 2005.
His research interests span computer vision, machine learning, and computer graphics, with a focus on visual recognition. He was awarded the David Marr Prize in 2009, a PASCAL VOC Lifetime Achievement Prize in 2010, an NSF Career Award in 2010, and the Outstanding Young Researcher in Image & Vision Computing Award in 2012. His work is supported by NSF, ONR, DARPA, as well as industrial collaborations with the Intel Science and Technology Center for Visual Computing, Google Research, and Microsoft Research. He serves on the editorial board of the International Journal of Computer Vision (IJCV), serves as associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), has served as a senior program committee member for the IEEE Conference of Computer Vision and Pattern Recognition (CVPR) and European Conference on Computer Vision (ECCV), and has served on multiple NSF panels for computer vision and machine learning.
Presentation: "Intelligent systems for analyzing visual data"
Abstract:
Visual media is being collected and stored at impressive rates. Such big visual data allow for the design of intelligent systems for processing such media, which in turn are spurring a variety of applications from every consumer organization of photo collections, visual diaries, to specialized applications for medical and military purposes. From a technical point-of-view, designing such systems can be difficult because the visual world can be complex and hard to analyze for an automated system. I will survey some contemporary approaches for intelligent anaylsis of based on statistical methods that exploit the existence of massively-large visual datasets.
