Hedvig Kjellström

I am a Professor in the Division of Robotics, Perception and Learning, KTH, and also affiliated with Silo AI, Swedish University of Agricultural Sciences, Swedish e-Science Research Centre, and Max Planck Institute for Intelligent Systems. A short bio is found here.

I do research in Computer Vision and Machine Learning. The general theme of my research is methods for enabling artificial agents to interpret human and animal behavior. As outlined in the Portfolio pages, these ideas are applied in the study of human aesthetic bodily expressions such as in music and dance, modeling and interpreting human communicative behavior, and the understanding of animal behavior and experiences. In order to accomplish this we develop methods for agents to perceive the world and build representations of it through vision.

I teach in the Bachelor program Engineering Mathematics and in the Master programs Machine Learning, Computer Science and Systems, Control and Robotics at KTH. My current courses are found below.

In my free time I play the double bass in different settings, more info here.

News

	On April 21, 2023, I was interviewed in Swedish-speaking Finnish radio about AI and large language models like Chat-GPT. Here is the interview (in Swedish).
	On December 20, 2022, I appeared in Swedish television and talked about AI, creativity and what I think we should worry about. Here is the interview (in Swedish).
	NEW PAPER: Felix Järemo Lawin, Anna Byström, Christoffer Roepstorff, Marie Rhodin, Mattias Almlöf, Mudith Silva, Pia Haubro Andersen, Hedvig Kjellström, and Elin Hernlund. Is Markerless More or Less? Comparing a smartphone computer vision method for equine lameness assessment to multi-camera motion capture. Animals, doi: 10.3390/ani13030390, 2023.
	NEW PAPER: Sofia Broomé, Marcelo Feighelstein, Anna Zamansky, Gabriel Carreira Lencioni, Pia Haubro Andersen, Francisca Pessanha, Marwa Mahmoud, Hedvig Kjellström, and Albert Ali Salah. Going deeper than tracking: A survey of computer-vision based recognition of animal pain and affective state. International Journal of Computer Vision, doi: 10.1007/s11263-022-01716-3 , 2023.
	NEW PAPER: Sofia Broomé, Ernest Pokropek, Boyu Li, and Hedvig Kjellström. Recur, Attend or Convolve? Frame dependency modeling matters for cross-domain robustness in action recognition. In IEEE Winter Conference on Applications of Computer Vision, 2023.