Professor Carnegie Mellon University | Google Research
43 YEARS OLD
Deva Ramanan is a highly respected professor and researcher in computer science, specializing in computer vision and machine learning. He currently holds the position of Professor in the Robotics Institute at Carnegie-Mellon University and is the director of the CMU Argo AI Center for Autonomous Vehicle Research.
Ramanan's research interests are broad, but his primary focus is visual recognition. His work earned him numerous accolades, including the David Marr Prize in 2009, the PASCAL VOC Lifetime Achievement Prize in 2010, and the IEEE PAMI Young Researcher Award in 2012. He was named one of Popular Science's Brilliant ten researchers in 2012 and a National Academy of Sciences Kavli Fellow in 2013.
In addition to these achievements, Ramanan has won several prestigious awards in recent years. He received the Longuet-Higgins Prize in 2018 for his fundamental contributions to computer vision and was recognized as a best paper finalist/honorable mention in CVPR 2019, ECCV 2020, and ICCV 2021.
Grants from various organizations, including NSF, ONR, and DARPA, support Ramanan's work. He collaborates with several industry partners, including Intel, Google, and Microsoft.
Beyond his research, Ramanan is actively involved in the computer science community. He served as the program chair of the IEEE Computer Vision and Pattern Recognition (CVPR) 2018 and is on the International Journal of Computer Vision (IJCV) editorial board. He also regularly serves as a senior program committee member for CVPR, ICCV, and ECCV and sits on NSF panels for computer vision and machine learning.
Summary of recent tweets
Deva Ramanan, a renowned AI researcher, has been active on Twitter recently. Analyzing his tweets, it is evident that he has been sharing insights and opinions related to computer vision and machine learning. His recent attitude appears to be focused and enthusiastic about advancements in these fields. He engages with the community through replies and retweets.
One noticeable trend in Deva Ramanan's tweets is his emphasis on the importance of data quality for training machine learning models. He highlights the need for clean, diverse, and unbiased datasets to ensure fair and accurate results. In one tweet, he shares a link to an article discussing the impact of biased training data on facial recognition systems.
Furthermore, Ramanan frequently mentions the potential applications of computer vision technology beyond traditional domains. He expresses excitement about how it can contribute to solving real-world problems like environmental conservation or healthcare diagnostics. A tweet featuring an innovative use case of using drones equipped with computer vision algorithms for wildlife conservation received significant attention with numerous retweets and replies.
In terms of mood, Deva Ramanan's tweets convey optimism and curiosity as he explores new developments in AI research. He often engages in discussions with fellow researchers, providing insightful comments or asking thought-provoking questions.
Despite being well-known within the AI community, there are no indications suggesting that Deva Ramanan has been inactive on Twitter lately. As of today (08/07/2023), his regular tweeting activity showcases his continued involvement in sharing knowledge and participating in conversations surrounding computer vision and machine learning advancements.
SOME AI BOOK RECOMMENDATIONS
Deva Ramanan hasn't written a book yet or we didn't find any ISBN number for their book(s).
However, here are some popular books in AI:
Videos Featuring Professor Deva Ramanan
CVPR23 E2EAD | Deva Ramanan, Invited Talk
Deva Ramanan - 6th BMTT Workshop ICCV 2021
FOVEA Overview (ICCV '21)
[CVPR'21 WAD] Keynote - Deva Ramanan, Argo/CMU
CVPR 2020 Workshop: Deva Ramanan
Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion
MCS 2020. Day 1. Deva Ramanan
MCS 2020. Day 1. Discussion
Deva Ramanan - Understanding Visual Appearances in the Long-tail
RI Seminar: Deva Ramanan : Recognizing objects using model-based statistics