Space weather forecasting
Automatic event identification
Feature detection and tracking
Machine and Deep Learning
Combination of physics-based and data-driven modeling
ABSTRACT SUBMISSION IS NOW OPEN
Deadline 1st April 2019
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.
Register your interest hereDownload the poster
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
1st April 2019: Abstract submission deadline
TBD: Early bird registration
TBD: Deadline to apply for travel support
TBD: Registration deadline
TBD: Hotel reservation deadline
16-20 September 2019: Conference
The abstract submission is open. The deadline is April 1st 2019.
Abstracts can be submitted through this link.
There is no limit to the number of abstracts an author can submit.
When submitting an abstract you will be asked to indicate at least three keywords relevant to your work among the following list: