Bilde av Wickstrøm, Kristoffer
Bilde av Wickstrøm, Kristoffer
Associate Professor / Machine Learning Department of Physics and Technology kristoffer.k.wickstrom@uit.no +4777623216 Tromsø You can find me here

Kristoffer Wickstrøm


Job description

I am an associate professor at UiT The Arctic University of Norway and the group leader of the UiT Machine Learning Group. My research primarily focuses on deep learning, particularly explainability and learning with limited labels. Additionally, I am involved in various professional roles and collaborations, as well as contributing to the broader machine learning community. Below is a summary of my roles, research interests, and past collaborations:

Current Roles:

  • Associate Professor at UiT The Arctic University of Norway.
  • Group Leader of the UiT Machine Learning Group.

Principal Investigator in:

  • SFI Visual Intelligence.
  • SFF Integreat.

Research Interests:

  • Deep learning
  • Explainability.
  • Learning with limited labels.
  • Uncertainty modeling.
  • Uncertainty modeling.
  • Information theory

See Google Scholar for a list of my publications.
Visit my personal website for more information.


  • Thea Brüsch, Kristoffer Wickstrøm, Mikkel N. Schmidt, Robert Jenssen, Tommy Sonne Alstrøm :
    FLEXtime: Filterbank Learning to Explain Time Series
    Communications in Computer and Information Science (CCIS) 14. October 2025 DOI
  • Teresa Dorszewski, Lenka Tětková, Robert Jenssen, Lars Kai Hansen, Kristoffer Knutsen Wickstrøm :
    From Colors to Classes: Emergence of Concepts in Vision Transformers
    12. October 2025 DOI
  • Jing Wang, Songhe Feng, Kristoffer Wickstrøm, Michael Kampffmeyer :
    AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering
    Computer Vision and Pattern Recognition 2025 DOI
  • Duy Khoi Tran, Van Nhan Nguyen, Kristoffer Wickstrøm, Michael Kampffmeyer :
    WOODWORK: A deep-learning based framework for woodpecker damage detection in powerline inspection
    International Journal of Electrical Power & Energy Systems 01. October 2025 DOI
  • Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba Amina Salahuddin, Kristoffer Wickstrøm et al.:
    Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
    Lecture Notes in Computer Science (LNCS) 20. September 2025 DOI
  • Kristoffer Wickstrøm, Hedström, Anna, Marina Marie-Claire Höhne :
    From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation | SpringerLink
    Lecture Notes in Computer Science (LNCS) 2025 DOI
  • Kristoffer Wickstrøm, Thea Brüsch, Michael Kampffmeyer, Robert Jenssen :
    REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability | Proceedings of the AAAI Conference on Artificial Intelligence
    Proceedings of the AAAI Conference on Artificial Intelligence 11. April 2025 DOI
  • Lars Uebbing, Harald Lykke Joakimsen, Luigi Tommaso Luppino, Iver Martinsen, Andrew McDonald, Kristoffer Wickstrøm et al.:
    Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting
    Proceedings of Machine Learning Research (PMLR) 2025 DOI
  • Suaiba Amina Salahuddin, Elisabeth Wetzer, Kristoffer Wickstrøm, Solveig Thrun, Michael Kampffmeyer, Robert Jenssen :
    Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance
    Lecture Notes in Computer Science (LNCS) 16. June 2025 DOI
  • Bjørn Møller, Christian Igel, Kristoffer Knutsen Wickstrøm, Jon Sporring, Robert Jenssen, Bulat Ibragimov :
    Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
    Proceedings of Machine Learning Research (PMLR) 2024 ARKIV / SAMMENDRAG / OMTALE
  • 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 DOI / 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 DOI / ARKIV
  • 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 DOI / ARKIV
  • 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 DOI / ARKIV
  • 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 DOI / ARKIV
  • 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
  • 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 DOI / ARKIV
  • Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba Amina Salahuddin, Xin Wang et al.:
    Reconsidering Spatial Alignment for Longitudinal Breast Cancer Risk Prediction
    07. December 2025
  • Christian Salomonsen, Kristoffer Wickstrøm, Samuel Kuttner, Elisabeth Wetzer :
    Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images
    16. October 2025
  • Christian Salomonsen, Kristoffer Wickstrøm, Samuel Kuttner, Elisabeth Wetzer :
    Physics-informed deep learning for improved input function estimation in motion-blurred dynamic [18F]FDG PET images
    24. September 2025
  • Johan Mylius-Kroken, Kristoffer Wickstrøm, Elisabeth Wetzer, Ali Ramezani-Kebrya, Robert Jenssen :
    Can a Convex Partition caused by a CPWL Neural Network be used for Density Estimation?
    National conference on image processing and machine learnin 2025
  • Christian Salomonsen, Kristoffer Wickstrøm, Samuel Kuttner, Elisabeth Wetzer :
    Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images
    27. September 2025
  • Lars Uebbing, Harald Lykke Joakimsen, Kristoffer Wickstrøm, Michael Kampffmeyer, Sebastien Francois Lefevre, Arnt Børre Salberg et al.:
    NOFE - Neural Operator Function Embedding
    2025
  • Lars Uebbing, Harald Lykke Joakimsen, Kristoffer Wickstrøm, Michael Kampffmeyer, Sebastien Francois Lefevre, Arnt Børre Salberg et al.:
    NOFE Neural Operator Function Embedding
    2025 DOI
  • Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba Amina Salahuddin, Kristoffer Wickstrøm et al.:
    Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
    20. September 2025
  • Simen Strømme, Kristoffer Wickstrøm, Kristoffer Søvik, Geir Lippestad, Johannes Bergh :
    Politiske partier har lansert KI-chatboter
    13. May 2025 DOI
  • Elisabeth Wetzer, Youssef Wally, Artem Galushko, Elisavet Kozyri, Kristoffer Wickstrøm :
    How to Tackle Bias and Protect Privacy in the Age of AI?
    17. September 2025
  • Christian Salomonsen, Kristoffer Wickstrøm, Elisabeth Wetzer, Samuel Kuttner :
    Physics-Informed Machine Learning for dynamic PET modeling
    2025
  • Christian Salomonsen, Kristoffer Wickstrøm, Elisabeth Wetzer, Samuel Kuttner :
    Physics-informed deep learning for predicting the arterial input function in dynamic PET imaging
    2025
  • Suaiba Amina Salahuddin, Elisabeth Wetzer, Kristoffer Wickstrøm, Solveig Thrun, Michael Kampffmeyer, Robert Jenssen :
    Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance
    2025
  • Christian Salomonsen, Kristoffer Wickstrøm, Elisabeth Wetzer, Samuel Kuttner :
    Physics-informed deep learning for improved input function estimation in motion-blurred dynamic [18F]FDG PET images
    2025
  • Petter Bjørklund, Kristoffer Knutsen Wickstrøm, Keyur Radiya :
    Finner leverkreft med kunstig intelligens
    uit.no 2024
  • Robert Jenssen, Kristoffer Knutsen Wickstrøm, Petter Bjørklund :
    Slik kan kunstig intelligens hjelpe legene
    Forskning.no 2024
  • Kristoffer Knutsen Wickstrøm :
    Kunstig intelligens fra beslutningstøtte til beslutningstaker – hvem er ansvarlig når ting går galt?
    2024
  • Kristoffer Knutsen Wickstrøm :
    Forskningsfronten på kunstig intelligens
    2024
  • Lars Uebbing, Harald Lykke Joakimsen, Luigi Tommaso Luppino, Iver Martinsen, Andrew McDonald, Kristoffer Knutsen Wickstrøm et al.:
    Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting
    2024
  • Kristoffer Knutsen Wickstrøm :
    Muligheter og utfordringer for kunstig intelligens i næringslivet
    2024
  • Petter Bjørklund, Robert Jenssen, Kristoffer Knutsen Wickstrøm :
    KI har superkrefter som kan hjelpe legene våre
    uit.no 2024 FULLTEKST
  • Christian Salomonsen, Kristoffer Knutsen Wickstrøm, Elisabeth Wetzer, Samuel Kuttner :
    Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
    2024
  • Christian Salomonsen, Kristoffer Knutsen Wickstrøm, Elisabeth Wetzer, Samuel Kuttner :
    Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
    2024
  • Christian Salomonsen, Kristoffer Knutsen Wickstrøm, Elisabeth Wetzer, Samuel Kuttner :
    Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
    2024
  • Kristoffer Knutsen Wickstrøm :
    Morrasendinga fra NRK i Troms - Forskere ved UiT Norges arktiske universitet utvikler kunstig intelligens til å finne leverkreft.
    10. June 2024
  • Kristoffer Knutsen Wickstrøm :
    Facebook skal merke KI-bilder: – Stor nyhet
    07. February 2024
  • Kristoffer Knutsen Wickstrøm :
    Kunstig Intelligens, utfordringer og muligheter for næringslivet
    2024
  • Robert Jenssen, Kristoffer Knutsen Wickstrøm :
    Ja takk til «krysskulturelle» prosjekter drevet fram av teknologiutvikling
    2023
  • Robert Jenssen, Kristoffer Knutsen Wickstrøm :
    Hvordan bør fotavtrykket av regjeringens satsing på kunstig intelligens se ut i Nord-Norge i 2030?
    2023
  • Kristoffer Wickstrøm :
    The aggregation method matters in faithfulness evaluation of XAI
    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 :
    XAI for time series analysis
    2023
  • Kristoffer Knutsen Wickstrøm :
    XAI for understanding of SSL representations
    2023

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    Research interests

    Deep learning.
    Explainable artificial intelligence
    Uncertainty analysis.
    Medical image analysis.
    Learning with limited labeles.


    Member of research group



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