Friday, March 8, 2019 at 12:00pm to 1:30pm
Learning Machines Seminar Series
Additional talk details TBA.
Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin. Her research in computer vision and machine learning focuses on visual recognition and search. Before joining UT-Austin in 2007, she received her Ph.D. at MIT. She is an Alfred P. Sloan Research Fellow and Microsoft Research New Faculty Fellow, a recipient of NSF CAREER and ONR Young Investigator awards, the Regents' Outstanding Teaching Award from the University of Texas System in 2012, the PAMI Young Researcher Award in 2013, the 2013 Computers and Thought Award from the International Joint Conference on Artificial Intelligence (IJCAI), the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013, and the Helmholtz Prize in 2017. She was inducted into the UT Academy of Distinguished Teachers in 2017. She and her collaborators were recognized with the CVPR Best Student Paper Award in 2008 for their work on hashing algorithms for large-scale image retrieval, the Marr Prize at ICCV in 2011 for their work on modeling relative visual attributes, the ACCV Best Application Paper Award in 2016 for their work on automatic cinematography for 360 degree video, and a Best Paper Honorable Mention at CHI in 2017 for work on crowds and visual question answering. She currently serves as an Associate Editor in Chief for the Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and as an Editorial Board member for the International Journal of Computer Vision (IJCV). She also served/serves as a Program Chair of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 and a Program Chair of Neural Information Processing Systems (NIPS) 2018.