Tomas Landelius has a PhD in Computer Vision from Linköping University (LiU). His thesis focused on reinforcement learning and he helped develop the first machine learning undergraduate course at LiU in 1996. Following his work at LiU, Tomas came to the Swedish Meteorological and Hydrological Institute (SMHI) in 1997. Here he developed a solar radiation model using an MLP to model the cloud effects. During his time at SMHI Tomas has been active in several national and EU projects concerning reanalysis (UERRA), data assimilation (IMPREX) and machine learning (DestinationEarth), often with connections to renewable energy sources. Tomas is a SMHI representative in the Swedish reference group for Horizon Europe cluster 4 – Digital, Industry & Space and in the International Energy Agency’s Photovoltaic Power Systems Program Task 16: Solar Resource for High Penetration and Large Scale Applications. Recently he started collaborating on machine learning for weather and climate prediction with the Division of Statistics and Machine Learning, Department of Computer and Information Science at LiU. A result of this is the first graph-based neural weather prediction model for limited areas. Currently Tomas is taking part in European efforts to make machine learning weather prediction models a natural part of the toolbox of a national meteorological service. In the future he’d like to explore the implications of the manifold hypothesis for probabilistic data assimilation and latent space prediction using sequential Monte Carlo methods and solutions to the Liouville and Fokker-Plank equations.