The goal of this first ML- Helio conference is to leverage the advancements happening in disciplines such as machine learning, deep learning, statistical analysis, system identification, and information theory, in order to address long-standing questions and enable a higher scientific return on the wealth of available heliospheric data.

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 Bain
Monica Bobra
Jacob Bortnik
Enrico Camporeale
Mark Cheung
Veronique Delouille
Farzad Kamalabadi
Michael Kirk
Giovanni Lapenta
Stefan Lotz
Sophie Murray
Bala Poduval
Pete Riley
Simon Wing

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