Machine Learning in Geospace

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VGEM 2021

Workshop Overview


We are back for a second year organizing the Machine Learning in Geospace session at the VGEM 2021 Workshop. This year, we have two sessions. One will be filled with invited and submitted talks and the other will focus on organizing a Kaggle Competition within the community to address a large, inter-disciplinary science problem using machine learning and data science techniques. See our Schedule Overview here and our detailed schedule below.


Register for the Workshop by filling out this form. There is no fee for students. Others can pay the $25 registration fee here.


The workshop begins on July 25 and ends on July 30.

Table 1: Workhop schedule. For more details see the Workshop webpage and our agenda below.
Event Date Day Time (Eastern)
Student Day July 25 Sunday 1:00 - 5:00 PM
VGEM Workshop July 26 - 30 Monday - Friday 11:00 AM - 4:30 PM + Posters
ML Session I July 28 Wednesday 3:00 - 4:00 PM
ML Session II July 30 Friday 1:00 - 2:30 PM


Table 2: Session organizers.
Name Email
Matthew Argall
Jacob Bortnik
Josh Rigler
Jason Shuster
Doğa Ozturk
Wendy Carande


Session I: Submitted Talks

Table 3: Speakers and presentation titles.
Duration Name Title
10 min Matthew Argall Session overview, decadal planning
10 min Breakout Rooms Ice Breaker
10 min Ayris Narock Ethical AI
6 min Matthew Blandin Prediction of Geomagnetic Field Disturbances across Alaska using Machine Learned LSTM Neural Networks
6 min Sheng Huang Recurrent neural network implementation of modelling global plasmaspheric density
6 min Michael Coughlan Using OMNI and SuperMag data to determine the risk of db/dt threshold exceedance with Long-Short Term Memory and Convolutional Neural Networks.
6 min Chris Bard Reconstructing MHD with Neural Networks
6 min Anthony Saikin NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux
6 min Eric Donovan Applying ML to the THEMIS-ASI data set
30 min Open Discussion

Session II: Kaggle Competition

Table 3: Speakers and presentation titles.
Duration Name Title
5 min Matthew Argall Session overview
15 min Addison Howard How to Design a Successful Kaggle Competition
5 min Manoj Nair NOAA's DataDriven MagNet Chellenge
5 min Barbara Thompson AI-Ready Datasets
5 min Raphael Attie Data classification tools
5 min Wendy Carande Getting Started with ML: Common Problems
20 min Breakout Rooms Designing the competition.
* What problems can be addressed with an ML Competition?
* What missions or datasets would be useful?
* What resources are required to prepare for a successful competition?
* What evaluation criteria will lead to a fair and meaningful winner?
* Get the discussion started early by contributing to our Jam Board
15 min Andrés Muñoz-Jaramillo
Enrico Camporeale
Shasha Zou
"What & How" A recap of the breakout sessions
20 min Speakers & Panelists Open Discussion on Kaggle Competition

Open Discussion

Heliophysics Decadal Survey White Papers

Members of the Heliophysics Community are coordinating and organizing white papers on Machine Learning and Data Science for the Heliophysics Decadal Survey in order to increase their visibility and impact. Anyone interested in leading, contributing to, or supporting a white paper, please reach out to Matthew Argall for more details.

VGEM 2020

Workshop Overview

Student Day Monday, July 20th, 2020 is Student Day
GEM Workshop Tuesday - Thursday, July 21st - 23rd

Participants will need to register for free here, which is accessible through the general meeting website. Our session agenda is posted below.

Machine Learning Session


Thursday, July 23, 3:00 - 4:30pm Eastern

Session Conveners

Name Email
Matthew Argall
Jacob Bortnik
Josh Rigler
Jason Shuster


We have worked with the GEM Student Representatives to incorporate a machine learning tutorial into their Student Day. Abby Azari will give a "getting started with machine learning" tutorial.

Session Overview and Theme

Beyond the mechanics of getting started, to fully take advantage of the power of machine learning, one needs to know what is possible. This is our session's theme -- to provide an overview of what is possible. This theme encompasses five focus areas:

  1. Recent developments in machine learning that enable innovative scientific research
  2. Aspects of machine learning or data science that are under-utilized in the field
  3. Systems/chains of machine learning models and/or the integration of machine learning into numerical simulations
  4. Possibilities and opportunities that exist outside of a purely academic career path enabled by machine learning
  5. Bridging the SHINE, GEM, and CEDAR communities to facilitate multi-discipline collaborations

The hope is that recent developments in ML will inspire new avenues of research and career opportunities.

Presentations and Panel Discussion

Speakers and panelists are encouraged to address any of the focus areas 1-4 and to include elements that enable focus area 5.

ML Exploratory Committee

This session is chaired by members of a SHINE/GEM/CEDAR Machine Learning Exploratory Committee. The committee is composed of members of the SHINE, GEM, and CEDAR communities. Its goal is to pursue topics in Heliophysics Machine Learning that can best be addressed through a cross-disciplinary initiative. Anyone interested can contact us for more information.



Table 1: Schedule for the Machine Learning session at VGEM. The audience can direct additional questions to the speakers during the panel discussion.
Duration Topic Speaker
5 min Opening remarks Matthew Argall
15 min Introduction to ML: "What is Possible?" Jacob Bortnik
(See Table 3)
7 min
3 min
Application(s): ML in GEM with relevance to SHINE/CEDAR Enrico Camporeale
7 min
3 min
Application of Machine Learning to GNSS-Related Remote Sensing Tasks Leo Liu
7 min
3 min
Predicting Solar Eruptions: What has ML done for us? Sophie Murray
40 min Open Panel Discussion See Table 2

Panel Discussion

Table 2: After a brief self-introduction, panelists to field questions in an open discussion format.
Field Panelist Early Career Theme
SHINE Wendy Carande Y Pairing science problems with ML tools
SHINE Barbara Thompson NASA, Policy
GEM Simon Wing Information Theory
GEM Xiangning Chu Y Global Reconstruction
CEDAR Bharat Kunduri Y Insight
Industry to academia
Planetary Abby Azari Y Student tutorial
Physics Informed ML
Interpretable ML
Industry Nick Bunch Y Academia to industry

Distributed Tutorial

Table 3: SHINE/GEM/CEDAR ML Exploratory Committee topics highlighting a subset of what is possible with machine learning. Each speaker provides an overview of the topic in ~1 minute and ~1 slide.
Speaker Topic
Ryan McGranaghan Community-wide inventory of assets and resources
Marcus Hughes Exploration, visualization and handling of large observational and modeling data volumes
Hazel Bain Understanding the potential enhancement of space weather prediction capabilities for operations and research
Barbara Thompson Creation of AI-ready data sets and quality standards
Jacob Bortnik Different approaches to implementing “Physics-informed learning” and “gray box models”
Josh Rigler Compression, acceleration and emulation of models and data processing
Matthew Argall Providing paths to publication, validation, and open-code practices