Deva Ramanan is a Professor in the Robotics Institute at Carnegie-Mellon University and the director of the CMU Argo AI Center for Autonomous Vehicle Research. 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, the IEEE PAMI Young Researcher Award in 2012, named one of Popular Science’s Brilliant 10 researchers in 2012, named a National Academy of Sciences Kavli Fellow in 2013, won the Longuet-Higgins Prize in 2018 for fundamental contributions in computer vision, and was recognized for best paper awards in CVPR 2019, ECCV 2020, and ICCV 2021. His work is supported by NSF, ONR, DARPA, as well as industrial collaborations with Intel, Google, and Microsoft.
He served at the program chair of the IEEE Computer Vision and Pattern Recognition (CVPR) 2018. 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 CVPR, the 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.
His research focuses on computer vision, often making heavy use of machine learning techniques and often using the human visual system as inspiration. For example, temporal processing is a key component of human perception, but is still relatively unexploited in current visual recognition systems. Machine learning from big (visual) data allows systems to learn subtle statistical regularities of the visual world. But humans have the ability to learn from very few examples.