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 models 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.
We expect all the participants of Machine Learning in Heliophysics to follow our Code of Conduct.
Daniel Baker, University of Colorado
Joe Borovsky, Space Science Institute
Cyril Furtlehner, INRIA Paris
George Karniadakis, Brown University
Adam Lesnikowski, NVIDIA
Robert McPherron, UCLA
Naoto Nishizuka, NICT (Japan)
Barbara Thompson, NASA Goddard
Peter Wintoft, Swedish Institute of Space Physics
Registration is open on this link
Single-day conference ticket: 150 EUR
Full rate: 500 EUR
The registration fee includes an opening reception and lunches from Monday to Friday. Any attendee can only purchase one single-day conference ticket.
Invitation letter: In case you need an invitation letter, you can make a request to email@example.com. Note that invitation letters can only be issued for registered participants.
The abstract submission is now close.
Special Issue in Frontiers in Astronomy and Space Sciences
This Research Topic calls for contributions pertaining to the application of machine learning in any subfield of Heliophysics. Works that have already been presented at the ML-Helio conference are welcome. However, the call is open to all contributors, and not limited to conference participants.
Authors can choose between two type of contributions:
1) a full-length research article (12,000 words and 15 Figures max) 2) a brief research report (4,000 words and 4 Figures max).
In either case the paper is expected to contain novel and original research, and the guest editors will strive to ensure a rapid peer-review and publication timeline.
Further information can be found on the journal website
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