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 here

Download the poster


Invited Speakers

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


Coming soon...
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Important dates

14 April 2019:  Abstract submission deadline
31 May:  Early bird registration
TBD:  Deadline to apply for travel support
TBD:  Registration deadline
TBD:  Hotel reservation deadline
16-20 September 2019:  Conference
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The abstract submission is open. The deadline is April 14th, 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:

  • Active regions
  • Adversarial Networks
  • Autoencoders
  • Autoregressive methods
  • Bayesian methods
  • Classification
  • Clustering
  • Compressed Sensing
  • Convolutional Neural Networks
  • Coronal holes
  • Coronal Mass Ejection (CME)
  • Data Assimilation
  • Data imbalance
  • Data reconstruction
  • Density Estimation
  • Dimensionality reduction
  • Energetic Particles
  • Ensemble methods
  • Event catalog mining
  • Feature detection
  • Flares
  • Gaussian Processes
  • Geomagnetic indexes
  • Geomagnetically Induced Currents
  • Helioseismology
  • Information theory
  • Inversion techniques
  • Ionosphere
  • Kalman Filter
  • Long Short-Term Memory
  • Magnetosphere-Ionosphere-Thermosphere
  • MHD
  • Monte Carlo methods
  • Neural Networks
  • Numerical Simulations
  • Performance metrics and Model validation
  • Photosphere-chromosphere
  • Probabilistic forecast
  • Radiation Belt
  • Recurrent Neural Networks
  • Regression
  • Ring current
  • Solar corona
  • Solar cycle
  • Solar magnetic field
  • Solar Wind
  • Space Weather forecasting
  • Spectropolarimetry
  • Substorm
  • Sunspot
  • Support Vector Machines
  • Support Vector Regression
  • Time-series analysis
  • Tracking
  • Uncertainty Quantification
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Scientific Organizing Committee

Hazel Bain (CU Boulder)
Monica Bobra (Stanford)
Jacob Bortnik (UCLA)
Enrico Camporeale (CU/CWI, chair)
Mark Cheung (LMSAL)
Veronique Delouille (ROB)
Farzad Kamalabadi (U. Illinois)
Michael Kirk (NASA)
Giovanni Lapenta (KU Leuven)
Stefan Lotz (SANSA)
Sophie Murray (Trinity College Dublin)
Bala Poduval (CWI)
Pete Riley (Predictive Science Inc.)
Simon Wing (APL, Johns Hopkins)

Local Organizing Committee

Mandar Chandorkar
Bala Poduval
Rakesh Sharma
Carl Shneider
Jannis Teunissen
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Venue & Travel

The conference will be held at The Royal Tropical Institute - Koninklijk Instituut voor de Tropen (KIT), a short walk from the 17th century UNESCO World Heritage Amsterdam Canal Ring.
Address: Mauritskade 64, 1092 AD Amsterdam


The Amsterdam (Schiphol) Airport is served by all major airlines. It is about 30 minutes from the city center, by train, bus or taxi. See here for transfer options.


For Hotel registration please click here


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