Joel Oskarsson
I am a PhD-student at the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Sweden. 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.
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.
I got my masters’s degree in computer science and engineering from Linköping University in 2020. In 2018-2019 I spent a year as an exchange student at ETH Zurich, Switzerland. I grew up in Lindesberg, Sweden and currently live in Linköping.
My CV is available here.
Current interests
These are some things that I am interested in and/or work on at the moment. I try to keep this somewhat up to date.
- Spatio-temporal data analysis
- Machine learning for modeling weather and climate
- Modeling continuous time signals using deep learning, Neural ODEs
- Machine learning on graphs
- Bayesian modeling on graphs, Graph GPs, GMRFs
- (Spatio-) Temporal graph neural networks
News
Mar 5, 2024 | I had the pleasure to visit the Department of Earth Sciences at Uppsala University and give a talk about our work on neural weather prediction. |
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Jan 11, 2024 | I gave a talk about Neural LAM weather models at the webinar “Deep Learning for Weather-Based Power Prediction”, organized by IEA Wind Task 51. A recording is available on youtube and my slides can be found here. |
Oct 30, 2023 | I am visiting the Sustainability and Machine Learning Group at University College London throughout November (until 2/12). |
Oct 10, 2023 | I had the pleasure to give a talk at the Danish Meteorological Institute about our work on Neural weather prediction for limited area modeling. Slides are available here. |
Oct 2, 2023 | New preprint on “Graph-based Neural Weather Prediction for Limited Area Modeling”. Now also accepted to the Tackling Climate Change with Machine Learning workshop @ NeurIPS 2023! Code is available on github. |
Sep 4, 2023 | I attended the very exciting workshop Large-scale deep learning for the Earth system (webpage) and presented some ongoing work in collaboration with the Swedish meteorological and hydrological institute. Slides are available here. |
Jun 22, 2023 | I presented our work on Bayesian Learning on Graphs using Deep Gaussian Markov Random Fields at the NORDSTAT conference. Slides are available here. |
Jun 2, 2023 | Together with great collaborators at the Division of Vehicular Systems I have two papers on trajectory prediction accepted for publication:
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May 10, 2023 | I presented my half-time-PhD seminar on “Graph-Based Machine Learning for Spatio-Temporal Data”. My slides are shared here. |
Feb 10, 2023 | New preprint: “MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs” |
Jan 20, 2023 | Our paper “Temporal Graph Neural Networks for Irregular Data” has been accepted to AISTATS 2023! Code is available on GitHub. |
Nov 14, 2022 | Together with the WASP graduate school I spent a week in Helsinki, visiting Aalto University and the Finnish Center for Artifical Intelligence. |
Jul 17, 2022 | I attended ICML 2022 in Baltimore, US |
Jul 4, 2022 | Conference paper + workshop paper accepted to ICML 2022 (Read More) |
Jun 13, 2022 | I attended the Nordic Probabilistic AI Summer School in Helsinki, Finland |