Machine Learning in Geospace
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Contents
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VGEM
| 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
When
Thursday, July 23, 3:00 - 4:30pm Eastern
Session Conveners
| Name | ||
|---|---|---|
| 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:
- Recent developments in machine learning that enable innovative scientific research
- Aspects of machine learning or data science that are under-utilized in the field
- Systems/chains of machine learning models and/or the integration of machine learning into numerical simulations
- Possibilities and opportunities that exist outside of a purely academic career path enabled by machine learning
- 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
| 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
| 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 | TBD | - | - |
| Planetary | Abby Azari | Y | Student tutorial Physics Informed ML Interpretable ML |
| Industry | Nick Bunch | Y | Academia to industry |
Distributed Tutorial
| 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 |
