Prof. Hedvig Kjellström

• Hedvig Kjellström is a Professor of Computer Science and the head of the Department of Robotics, Perception, and Learning (RPL) at KTH in Stockholm, Sweden. She received an MSc in Engineering Physics and a PhD in Computer Science from KTH in 1997 and 2001, respectively. The topic of her doctoral thesis was 3D reconstruction of human motion in video. Between 2002 and 2006 she worked as a scientist at the Swedish Defence Research Agency, where she focused on Information Fusion and Sensor Fusion. In 2007 she returned to KTH, pursuing research in activity analysis in video. Her present research focuses on the modeling of perception and production of human non-verbal communicative behavior and activity, with applications in Social Robotics, Performing Arts, and Healthcare.

• In 2010, she was awarded the Koenderink Prize for fundamental contributions in Computer Vision for her ECCV 2000 article on human motion reconstruction, written together with Michael Black and David Fleet. She has written around 80 papers in the fields of Robotics, Computer Vision, Information Fusion, Machine Learning, Cognitive Science, Speech, and Human-Computer Interaction. She is mostly active within the areas of Robotics and Computer Vision, where she is an Associate Editor for IEEE TPAMI and IEEE RA-L, and an Area Chair for CVPR 2016, RSS 2016, ICCV 2017, and Humanoids 2017.

• Judith Bütepage, Michael J. Black, Danica Kragic, and Hedvig Kjellström. Deep representation learning for human motion prediction and classification. In IEEE Conference on Computer Vision and Pattern Recognition, 2017.

• Cheng Zhang, Hedvig Kjellström, and Carl Henrik Ek. Inter-battery topic representation learning. In European Conference on Computer Vision, 2016.

• Alessandro Pieropan, Carl Henrik Ek, and Hedvig Kjellström. Functional object descriptors for human activity modeling. In IEEE International Conference on Robotics and Automation, 2013.

• Hedvig Kjellström, Javier Romero, and Danica Kragic. Visual object-action recognition: Inferring object affordances from human demonstration. Computer Vision and Image Understanding, 115:81-90, 2011.

• Hedvig Kjellström and Olov Engwall. Audiovisual-to-articulatory inversion. Speech Communication, 51(3):195-209, 2009.

• Hedvig Sidenbladh. Multi-target particle filtering for the probability hypothesis density. In International Conference on Information Fusion, pp 800-806, 2003.

• Hedvig Sidenbladh, Michael J. Black, and Leonid Sigal. Implicit probabilistic models of human motion for synthesis and tracking. In European Conference on Computer Vision, vol 1, pp 784-800, 2002.

• Hedvig Sidenbladh, Michael J. Black, and David J. Fleet. Stochastic tracking of 3D human figures using 2D image motion. In European Conference on Computer Vision, vol 2, pp 702-718, 2000.

• EquestrianML: Machine Learning methods for recognition of the pain expressions of horses

  http://www.csc.kth.se/~hedvig/research.html#HORSE

• EACare: Embodied Agent to support elderly mental wellbeing

  http://www.csc.kth.se/~hedvig/research.html#EACARE

• FOVIAL: FOrensic VIdeo AnaLysis – finding out what really happened

  http://www.csc.kth.se/~hedvig/research.html#FOVIAL

• TOMSY: TOpology based Motion SYnthesis for dexterous manipulation

  http://www.csc.kth.se/~hedvig/research.html#TOMSY

• HumanAct: Visual and multi-modal learning of Human Activity and interaction with the surrounding scene

  http://www.csc.kth.se/~hedvig/research.html#HUMANACT

• Advisory Board member, L3S Research Center

  https://www.l3s.de/

• Management Group member, Swedish e-Science Research Centre

  http://e-science.se/

• Associate Editor, IEEE Robotics and Automation Letters

  http://www.ieee-ras.org/publications/ra-l

• Associate Editor, IEEE Transactions on Pattern Analysis and Machine Intelligence

  https://www.computer.org/web/tpami

• Sveriges Television, June 2017

  Live studio interview about face recognition

• Sveriges Television, May 2017

  Live studio interview about robotics

• Ruptly TV, August 2016

  Recorded interview about the EACare project

  https://www.youtube.com/watch?v=w2Y7yPYPFbE

• Dagens Nyheter, July 2016

  News article about the EACare project

  http://www.dn.se/nyheter/vetenskap/social-robot-ska-upptacka-demens-tidigare/

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