UiT Machine Learning Group

Pushing the frontier

Powered by the cool Arctic air, and located at 70° north, the core strength of the Machine Learning Group at UiT The Arctic University of Norway is in basic research for advancing statistical machine learning & AI methodology to face the societal and industrial data-driven challenges of the future.

We publish in CVPR, AAAI, ECCV, IEEE TPAMI, IEEE TNNLS, MICCAI, to name a few.

The UiT Machine Learning Group is hosting the annual Northern Lights Deep Learning Conference. Please see: nldl.org

We head SFI Visual Intelligence, a Centre for Research-based Innovation funded by the Research Council of Norway and consortium partners.


  • Kristoffer Olesen from the Technical University of Denmark joins the UiT Machine Learning Group for a research stay.

  • New paper by Ph.D. candidate Stine Hansen and collaborators accepted at the journal of Medical Image Analysis.

  • New paper by Ph.D. candidate Srishti Gautam and collaborators accepted at ISBI 2022.

  • The UiT Machine Learning Group was well represented with several talks during the 2021 Norwegian Society for Image Processing and Machine Learning conference that was arranged in Oslo.

  • Ph.D. candidate Ane Blázquez-García from the technology center Ikerlan joins the UiT Machine Learning Group for a research stay.

  • Director of Visual Intelligence Robert Jenssen gave a talk at the BIAS Summer school at the University of Bristol. The talk describes how deep learning is leveraged in computer vision in an industrial innovation project for monitoring power lines in Norway.

  • Robert Jenssen gave an overview of the priorities and activities of Visual Intelligence for health research for the Board of the Northern Norway Regional Health Authority (Helse Nord).

  • New paper at CVPR: Reconsidering Representation Alignment for Multi-view Clustering (25% acceptance rate).

  • Michael Kampffmeyer from the UiT Machine Learning Group awarded FRIPRO grant (ground breaking research) from the Research Council of Norway!

  • New paper at AAAI: Measuring Dependence with Matrix-based Entropy Functional (21% acceptance rate).

  • New paper! A novel Dissimilarity Measure for Prototypical Few-Show Learning Networks accepted for ECCV.