Hedvig Kjellström

Professor

Kungliga Tekniska Högskolanhttps://www.kth.se/profile/hedvig

Arbetar vid

Robotik, perception och lärande

Telefon

+46 8 790 69 06

Enhetens adress

Lindstedtsvägen 24

E-post

hedvig@kth.se

Rum

416c

Forskar-ID

ORCID IDWeb of Science ResearcherIDScopus IDGoogle Scholar IDResearchGate

Om mig

I am a Professor in the Department of Robotics, Perception and Learning, KTH, and also affiliated with Swedish e-Science Research Centre and Max Planck Institute for Intelligent Systems, Germany. A bio and cv is found here.

My main area of research is Computer Vision, which is a sub-field of AI. In my research I develop computer methods to observe and make inferences and predictions about humans and other animals. As outlined in the Portfolio pages, four partly overlapping themes in my research are Computational Aesthetics, AI for Life Science, AI for Animal Science, and Embodied AI.

I am an Editor-in-Chief for CVIU and was a Program Chair for CVPR 2025.

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

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

News

	Since 2023 I am part of the AI exhibition at Tekniska museet in Stockholm (in Swedish with English subtitles). I can really recommend a visit to this incredibly exciting museum, crossing science, technology, and art.
	On December 19, 2024, I took part in the radio show Förmiddag med Louise Epstein and talked about how AI and humans can live together. Here is the espisode (in Swedish).
	NEW PAPER: Mert Mermerci, Emile Pascoe, Fredrik Edström, and Hedvig Kjellström. Real-time control of a virtual orchestra by recognition of conducting gestures. arXiv preprint arXiv:2604.27957, 2026. Data and videos
	NEW PAPER: Ricardo Vinuesa, Paola Cinnella, Jean Rabault, Hossein Azizpour, Stefan Bauer, Bingni W. Brunton, Arne Elofsson, Elias Jarlebring, Hedvig Kjellström, Stefano Markidis, David Marlevi, Javier García-Martínez, and Steven L. Brunton. Decoding complexity through machine learning is redefining scientific discovery. Nature Communications Physics 9, 168, 2026.
	NEW PAPER: Zineb Senane, Axel Karlsson, Lele Cao, Oleg Smirnov, Cheng Zhang, Sahar Asadi, Hedvig Kjellström, Gustav Eje Henter, and Ruibo Tu. Causality for tabular data synthesis: A high-order structure causal benchmark framework. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2026.