Difference between revisions of "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 https://gemworkshop.org/pages/RegistrationVirtual.php], which is accessible through [the general meeting website https://gemworkshop.org/]. Our session agenda is posted below. | ||
| + | |||
| − | + | = Machine Learning Session = | |
| + | |||
| + | == When == | ||
| + | Thursday, July 23, 3:00 - 4:30pm Eastern | ||
| + | |||
| + | == Session Conveners == | ||
| + | |||
| + | {| | ||
| + | ! Name !! !! Email | ||
| + | |- | ||
| + | | Matthew Argall || || [mailto:matthew.argall@unh.edu matthew.argall@unh.edu] | ||
| + | |- | ||
| + | | Jacob Bortnik || || [mailto:jbortnik@gmail.com jbortnik@gmail.com] | ||
| + | |- | ||
| + | | Josh Rigler || || [mailto:erigler@usgs.gov erigler@usgs.gov] | ||
| + | |- | ||
| + | | Jason Shuster || || [mailto:jason.r.shuster@nasa.gov 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. | ||
Revision as of 10:02, 12 July 2020
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Contents
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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 https://gemworkshop.org/pages/RegistrationVirtual.php], which is accessible through [the general meeting website https://gemworkshop.org/]. 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.
