Event Location: NSH 1305
Bio: 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 under the supervision of David Forsyth.

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, the PASCAL VOC Lifetime Achievement Prize in 2010, an NSF Career Award in 2010, the Outstanding Young Researcher in Image and Vision Computing Award in 2012, and was selected as one of Popular Science’s Brilliant 10 researchers 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 has held visiting researcher positions at the Robotics Institute at CMU, the Visual Geometry Group at Oxford, and has been a consultant for Microsoft and Google.

He is on the editorial board of the International Journal of Computer Vision (IJCV) and is an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). He regularly serves as a senior program committee member for the IEEE Conference of Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), and the European Conference on Computer Vision (ECCV). He also regularly serves on NSF panels for computer vision and machine learning.

Abstract: Object recognition is a central task in computer vision. It is difficult because objects can vary greatly in appearance. Classic approaches tended to focus on geometric models inspired by computer graphics. Contemporary work follows a more statistical approach and learns models from “big” collections of training data. In this talk I will discuss a family of approaches that combine these schools of thought through the use of latent variable statistical models. These models produce state-of-the-art performance on a variety of established benchmark tasks including object detection, human pose estimation, and facial analysis.