Kristen Grauman

Kristen Grauman is a Full Professor in the Department of Computer Science at the University of Texas at Austin, where she leads the UT Computer Vision Group. Her research focuses on computer vision and machine learning, with applications in visual recognition, video analysis, and multi-modal perception.

She received her B.A. from Boston College and her Ph.D. from MIT.

Her research interests span visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning. She has made significant contributions to the field, particularly in developing algorithms for understanding visual content and human activities from video, including foundational work on the Pyramid Match Kernel and relative attributes.

Her recent publications reveal a strong emphasis on egocentric (first-person) vision, audio-visual learning, and view-invariant representations. There is a clear trajectory toward multi-modal integration (vision, audio, language) and real-world applications in instructional videos, human activity understanding, and embodied AI systems.

She has received numerous awards including:

• AAAI Fellow (2019)
• J. K. Aggarwal Prize, International Association for Pattern Recognition (2018)
• Helmholtz Prize (2017)
• UT Austin Academy of Distinguished Teachers (2017)
• Best Paper Award, Asian Conference on Computer Vision (2016)
• Presidential Early Career Award for Scientists and Engineers (2014)
• Computers and Thought Award, International Joint Conferences on Artificial Intelligence (2013)
• Pattern Analysis and Machine Intelligence Young Researcher Award (2013)
• Alfred P. Sloan Research Fellow (2012)
• Marr Prize (2011)

Prof. Grauman serves as Associate Editor-in-Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. She has secured substantial research funding including the Presidential Early Career Award, NSF grants, and industry partnerships. Her advising has produced numerous influential publications and students who are now leaders in computer vision.

She leads the UT Computer Vision Group, which collaborates closely with the Electrical and Computer Engineering Department. The group is pioneering large-scale egocentric video research through projects like Ego4D and Ego-Exo4D, focusing on real-world applications in human activity understanding, audio-visual perception, and interactive systems.