Computer Vision Laboratory Seminar: Deva Ramanan, "Embodied perception for open-world robotics"
Friday, May 17, 2019
3137 Iribe Center
Janice M. Perrone
Embodied perception for open-world robotics
Carnegie Mellon University
Lead of Perception at Argo AI
Computer vision is undergoing a period of rapid progress, rekindling its relationship and ties to robotics. In this talk, I will discuss various issues in perception motivated by autonomous robots "in-the-wild", focusing on the illustrative case of autonomous vehicles. Specific challenges include low-latency perception, generalization to rare scenarios, and self-aware processing that can recognize anomalous conditions. I will conclude with a description of open challenges in this domain.
Deva Ramanan is an associate professor at the Robotics Institute at Carnegie-Mellon University and the lead of Perception at Argo AI. Prior to joining CMU, he was an associate professor at UC Irvine. His research interests span computer vision and machine learning, 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 UCI Chancellor's Award for Excellence in Undergraduate Research in 2011, the PAMI Young Researcher Award in 2012, one of Popular Science's Brilliant 10 researchers in 2012, and the Longuet-Higgins Prize in 2018 for fundamental contributions in computer vision. His work is supported by NSF, ONR, DARPA, as well as industrial collaborations with Intel, Google, and Microsoft.
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 served as program chair of CVPR 2018. He also regularly serves on NSF panels for computer vision and machine learning.