Kristoffer Wickstrøm


Associate Professor / Machine Learning

Job description

I am an associate professor in the Machine Learning Group at UiT The Arctic University of Norway and a principal investigator in the SFI Visual Intelligence. My main research area is deep learning, with a particular focus on explainability and learning with limited labels. I am also interested in uncertainty modeling, information theory, kernel methods, and medical image analysis.

I have previously been a guest researcher in the Image Processing Laboratory at the University of Valencia with Professor Gustau Camps-Valls and in the Understandable Machine Intelligence Laboratory at Technical University of Berlin with Professor Marina M.-C. Höhne.

See Google Scholar for a list of my publications.


  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
    Computerized Medical Imaging and Graphics 2023 ARKIV / DOI
  • Kristoffer Knutsen Wickstrøm, Marina Marie-Claire Höhne :
    The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
    Transactions on Machine Learning Research (TMLR) 2023 ARKIV
  • Ane Blazquez-Garcia, Kristoffer Knutsen Wickstrøm, Shujian Yu, Karl Øyvind Mikalsen, Ahcene Boubekki, Angel Conde et al.:
    Selective Imputation for Multivariate Time Series Datasets with Missing Values
    IEEE Transactions on Knowledge and Data Engineering 2023 ARKIV / DOI
  • Daniel Johansen Trosten, Rwiddhi Chakraborty, Sigurd Eivindson Løkse, Kristoffer Wickstrøm, Robert Jenssen, Michael Kampffmeyer :
    Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
    Computer Vision and Pattern Recognition 2023 ARKIV / DOI
  • Eirik Agnalt Østmo, Kristoffer Wickstrøm, Keyur Radiya, Michael Kampffmeyer, Robert Jenssen :
    View it like a radiologist: Shifted windows for deep learning augmentation of CT images
    Machine Learning for Signal Processing 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Ahcene Boubekki, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    RELAX: Representation Learning Explainability
    International Journal of Computer Vision 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Sigurd Eivindson Løkse, Michael Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen :
    Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
    Entropy 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    Communications in Computer and Information Science (CCIS) 2022 ARKIV / DOI
  • Andreas Kvammen, Kristoffer Wickstrøm, Samuel Kociscak, Jakub Vaverka, Libor Nouzak, Arnaud Zaslavsky et al.:
    Machine learning detection of dust impact signals observed by the Solar Orbiter
    Annales Geophysicae 2022 DATA / ARKIV / DOI
  • Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen :
    Mixing up contrastive learning: Self-supervised representation learning for time series
    Pattern Recognition Letters 2022 ARKIV / DOI
  • Samuel Kuttner, Kristoffer Knutsen Wickstrøm, Mark Lubberink, Andreas Tolf, Joachim Burman, Rune Sundset et al.:
    Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function
    Journal of Cerebral Blood Flow and Metabolism 08. February 2021 ARKIV / DOI
  • Kristoffer Knutsen Wickstrøm, Karl Oyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen :
    Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
    IEEE journal of biomedical and health informatics 2021 ARKIV / DOI
  • Shujian Yu, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Jose Principe :
    Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration
    IEEE Transactions on Neural Networks and Learning Systems 2020 DOI
  • Samuel Kuttner, Kristoffer Knutsen Wickstrøm, Gustav Kalda, Seyed Esmaeil Dorraji, Montserrat Martin-Armas, Ana Oteiza et al.:
    Machine learning derived input-function in a dynamic 18F-FDG PET study of mice
    Biomedical Engineering & Physics Express 2020 ARKIV / DOI
  • Andreas Kvammen, Kristoffer Knutsen Wickstrøm, Derek McKay, Noora Partamies :
    Auroral Image Classification With Deep Neural Networks
    Journal of Geophysical Research (JGR): Space Physics 05. October 2020 ARKIV / DOI
  • Kristoffer Knutsen Wickstrøm, Michael C. Kampffmeyer, Robert Jenssen :
    Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
    Medical Image Analysis 2019 ARKIV / DOI
  • Kristoffer Knutsen Wickstrøm, Michael C. Kampffmeyer, Robert Jenssen :
    Uncertainty modeling and interpretability in convolutional neural networks for polyp segmentation
    IEEE Signal Processing Society 2018 ARKIV / DOI
  • Kristoffer Wickstrøm :
    The aggregation method matters in faithfulness evaluation of XAI
    2023
  • Kristoffer Knutsen Wickstrøm :
    XAI for understanding of SSL representations
    2023
  • Kristoffer Knutsen Wickstrøm :
    XAI for time series analysis
    2023
  • Andreas Kvammen, Kristoffer Wickstrøm, Samuel Kociscak, Jakub Vaverka, Libor Nouzák, Arnaud Zaslavsky et al.:
    Machine learning detection of dust impact signals
    2023
  • Kristoffer Knutsen Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Daniel Johansen Trosten, Kristoffer Wickstrøm, Shujian Yu, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    Deep Clustering with the Cauchy-Schwarz Divergence
    2022
  • Kristoffer Wickstrøm :
    Technical aspects of translating AI algorithms into real life medical practice, within the design and implementation of Randomized Controlled Trials
    2022
  • Kristoffer Wickstrøm :
    Hva gjør vi når kunstig intelligens gir oss kunnskap vi ikke forstår?
    Forskersonen.no 03. January 2022
  • Kristoffer Wickstrøm :
    Advancing Deep Learning with Emphasis on Data-Driven Healthcare
    UiT Norges arktiske universitet 2022
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    2022
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Explaining representations for medical image retrieval
    2022
  • Samuel Kuttner, Luigi Tommaso Luppino, Kristoffer Wickstrøm, Nils Thomas Doherty Midtbø, Seyed Esmaeil Dorraji, Ana Oteiza et al.:
    Deep learning derived input function in dynamic 18F-FDG PET imaging of mice
    2022
  • Kristoffer Wickstrøm :
    The past, present, and future of XAI
    2022
  • Kristoffer Knutsen Wickstrøm, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Towards Explainable Representation Learning
    2021
  • Kristoffer Knutsen Wickstrøm, Michael Kampffmeyer, Robert Jenssen :
    Advances in explainable DL & how to model uncertainty in explainability
    2021
  • Kristoffer Knutsen Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen :
    Uncertainty-Aware Deep Ensembles for Explainable Time Series Prediction
    2021

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    Member of research group