We aim at bringing together a cross-disciplinary research community: physicists in solar, heliospheric, magnetospheric, and aeronomy fields as well as computer and data scientists. ML- Helio will focus on the development of data science techniques needed to tackle fundamental problems in space weather forecasting, inverse estimation of physical parameters, automatic event identification, feature detection and tracking, times series analysis of dynamical systems, combination of physics-based model with machine learning techniques, surrogate models and uncertainty quantification.
The conference will consists of classic-style lectures, complemented by hands-on tutorials on Python tools and data resources available to the heliophysics machine learning community.
Scientific Organizing Committee
Hazel BainMonica BobraJacob BortnikEnrico CamporealeMark CheungVeronique DelouilleFarzad KamalabadiMichael KirkGiovanni LapentaStefan LotzSophie MurrayBala PoduvalPete RileySimon Wing
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