Machine Learning in Geospace

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VGEM

Student Day Sunday, July 20th, 2020 is Student Day
GEM Workshop Monday - 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

When

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

Session Conveners

Name Email
Matthew Argall matthew.argall@unh.edu
Jacob Bortnik jbortnik@gmail.com
Josh Rigler erigler@usgs.gov
Jason Shuster jason.r.shuster@nasa.gov

Preface

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.

Agenda

Session

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 about SHINE/GEM/CEDAR ML
Exploratory Committee and the ML Session
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(s): ML in CEDAR with relevance to GEM
7 min
3 min
Application(s): ML in SHINE with relevance to GEM 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 -
SHINE Barbara Thompson NASA, Policy
GEM Simon Wing Information Theory
GEM Xiangning Chu Y Global Reconstruction
CEDAR Bharat Kunduri Y Insight
Industry to academia
CEDAR - - -
Planetary Abby Azari Y Student tutorial
Physics Informed 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