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Personal profile

Research interests

Artificial Intelligence, Computer Vision, Machine Learning, Activity Analysis, Action Recognition, First-Person Vision, Egocentric Vision, Video Analysis

Short Bio...

Dima Damen is a Professor in Computer Vision at the University of Bristol. Dima is currently an EPSRC Fellow (2020-2025), focusing her research interests in the automatic understanding of object interactions, actions and activities using wearable visual (and depth) sensors. She has contributed to novel research questions including assessing action completion, skill/expertise determination from video sequences, discovering task-relevant objects, dual-domain and dual-time learning as well as multi-modal fusion using vision, audio and language. She is the project lead for EPIC-KITCHENS, the largest dataset in egocentric vision, with accompanying open challenges. She also leads the EPIC annual workshop series alongside major conferences (CVPR/ICCV/ECCV). Dima is a program chair for ICCV 2021, associate editor of IJCV, IEEE TPAMI and Pattern Recognition. She was selected as a Nokia Research collaborator in 2016, and as an Outstanding Reviewer in CVPR2021, CVPR2020, ICCV2017, CVPR2013 and CVPR2012. Dima received her PhD from the University of Leeds (2009), joined the University of Bristol as a Postdoctoral Researcher (2010-2012), Assistant Professsor (2013-2018), Associate Professor (2018-2021) and was appointed as chair in August 2021. She supervises 9 PhD students, and 5 postdoctoral researchers.

Awards and Fellowships

  • Outstanding Reviewer, CVPR 2021
  • Outstanding Reviewer, CVPR 2020
  • Outstanding Reviewer, ICCV, 2017
  • University Research Fellowship 2017/2018, University of Bristol, 2017
  • Nokia Research Collaborator, 2016
  • Outstanding Reviewer, IEEE CVPR, 2013
  • Outstanding Review, IEEE AVSS, 2013
  • Outstanding Reviewer, IEEE CVPR, 2012
  • Overseas Research Students Award Scholarship (ORSAS), 2006-2009
  • MSc Prize for Best Academic Performance, University of Leeds, 2003

More information: http://dimadamen.github.io

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