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Joel Oskarsson

PhD student

My research interests are in spatio-temporal and graph-based machine learning.

Presentation

I am a PhD-student at the Division of Statistics and Machine Learning. My main supervisor is Fredrik Lindsten and co-supervisors Per Sidén and Jose M. Peña. I am an affiliated PhD-student within the WASP program.

Research

In my research I aim to develop machine learning methods for structured data. I develop methods for data with spatial-, temporal- and graph-structure, including combinations of these. In particular, I am interested in how more traditional probabilistic methods in these domains can be combined with deep learning in order to derive new methods with useful properties.

For more information, please visit my webpage.

Publications

2025

Joel Oskarsson (2025) Modeling Spatio-Temporal Systems with Graph-based Machine Learning
Simon Adamov, Joel Oskarsson, Leif Denby, Tomas Landelius, Kasper Hintz, Simon Christiansen, Irene Schicker, Carlos Osuna, Fredrik Lindsten, Oliver Fuhrer, Sebastian Schemm (2025) Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings

2024

Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten (2024) Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks Advances in Neural Information Processing Systems: 38th Conference on Neural Information Processing Systems (NeurIPS 2024), p. 41577-41648 (Conference paper)

2023

Theodor Westny, Joel Oskarsson, Björn Olofsson, Erik Frisk (2023) MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs IEEE Transactions on Intelligent Vehicles, Vol. 8, p. 4223-4236 (Article in journal) Continue to DOI
Joel Oskarsson, Per Sidén, Fredrik Lindsten (2023) Temporal Graph Neural Networks for Irregular Data Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, p. 4515-4531 (Conference paper)

Research

About the Division

Colleagues at STIMA

About the Department