Publications

Papers

  1. building_lams.png
    Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
    Simon Adamov*, Joel Oskarsson*, Leif Denby, Tomas Landelius, Kasper Hintz, Simon Christiansen, Irene Schicker, Carlos Osuna, Fredrik Lindsten, Oliver Fuhrer, and Sebastian Schemm
    arXiv preprint , 2025
  2. cont_ens.png
    Continuous Ensemble Weather Forecasting with Diffusion models
    In International Conference on Learning Representations, 2025
  3. graph_efm.png
    Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
    In Advances in Neural Information Processing Systems, 2024
    Spotlight
  4. thumb_cp_surrogate_models.png
    Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction
    Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt Kusner, and Marc Peter Deisenroth
    arXiv preprint, 2024
  5. thumb_round.png
    MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
    Theodor Westny, Joel Oskarsson, Björn Olofsson, and Erik Frisk
    IEEE Transactions on Intelligent Vehicles, 2023
  6. thumb_evaluation_diff.png
    Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
    Theodor Westny, Joel Oskarsson, Björn Olofsson, and Erik Frisk
    In 2023 IEEE Intelligent Vehicles Symposium (IV), 2023
  7. thumb_periodic_tgnn.png
    Temporal Graph Neural Networks for Irregular Data
    Joel Oskarsson, Per Sidén, and Fredrik Lindsten
    In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023
  8. thumb_graph_dgmrf.png
    Scalable Deep Gaussian Markov Random Fields for General Graphs
    Joel Oskarsson, Per Sidén, and Fredrik Lindsten
    In Proceedings of the 39th International Conference on Machine Learning, 2022

Workshop papers



  1. diffusion_lam.png
    Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
    In ICLR 2025 Workshop on Tackling Climate Change with Machine Learning, 2025
  2. thumb_cp_neurwp.png
    Valid Error Bars for Neural Weather Models using Conformal Prediction
    Vignesh Gopakumar, Joel Oskarsson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt Kusner, and Marc Deisenroth
    In ICML Workshop on Machine Learning for Earth System Modeling, 2024
    Note: A substantial extension of this research is presented in our paper "Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction"
  3. thumb_hi_lam.png
    Graph-based Neural Weather Prediction for Limited Area Modeling
    Joel Oskarsson, Tomas Landelius, and Fredrik Lindsten
    In NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023
  4. thumb_temporal_gnns.png
    Temporal Graph Neural Networks with Time-Continuous Latent States
    Joel Oskarsson, Per Sidén, and Fredrik Lindsten
    In ICML Workshop on Continuous Time Methods for Machine Learning, 2022
    Note: A substantial extension of this research is presented in our paper "Temporal Graph Neural Networks for Irregular Data"

Theses

  1. Probabilistic Regression using Conditional Generative Adversarial Networks
    Joel Oskarsson
    Linköping University, The Division of Statistics and Machine Learning, 2020
    MSc Thesis