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Kristen Grauman

Also Known As Kristen Lorraine Grauman · Kristen L. Grauman
Computing Methodologies

Kristen Grauman (born 1979) is an American computer scientist known for research on visual recognition and egocentric video understanding. She earned a BS summa cum laude in computer science from Boston College in 2001 and an MS and PhD from the Massachusetts Institute of Technology in 2003 and 2006, the latter supervised by Trevor Darrell, developing the pyramid match kernel method for comparing sets of image features. She joined the University of Texas at Austin in 2007 as the Clare Boothe Luce Assistant Professor, earned tenure as associate professor in 2011, and has been on leave since May 2018 as a research scientist at Facebook AI Research. Her research addresses object categorization and detection, video summarization for first-person, egocentric footage, and human-in-the-loop approaches to computer vision. She received a Presidential Early Career Award for Scientists and Engineers and the IJCAI Computers and Thought Award in 2013, an NSF CAREER Award in 2015, the Helmholtz Prize in 2017, the International Association for Pattern Recognition's J.K. Aggarwal Prize in 2018, and was elected an IEEE Fellow and AAAI Fellow in 2019 and a Fellow of the American Association for the Advancement of Science in 2023.

Facts
Birth Year
1979 1
Award
Helmholtz Prize 1
Award
AAAI Fellow 1
Award
J. K. Aggarwal Prize 1
Award
Marr Prize 1
Award
Presidential Early Career Award for Scientists and Engineers 1
Connections

In Field

Source Kristen Grauman (Wikipedia)
Source Kristen Grauman (Wikipedia)
Sources
1. Wikidata: Kristen Grauman
  • Wikidata Q56580712, resolved via en.wikipedia pageprops (wave rule R-L)
  • Wikidata Q56580712 P569 (date of birth)
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Kristen Grauman (Wikipedia)
  • In Field: Artificial Intelligence, Lead sentence
    Kristen Lorraine Grauman is a professor of computer science at the University of Texas at Austin on leave as a research scientist at Facebook AI Research (FAIR).
  • In Field: Computer Vision, Lead paragraph
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