CEDAR email: AGU event to put on your schedule

[Ryan McGranaghan]

 Fellow CEDAR-goers

Happy holidays! Hoping you're well, all things considered.

We would like to bring to your attention a town hall on machine learning as
applied to space physics, to be held at the upcoming AGU meeting.  Details
below. We would be delighted to have you join us and enrich the discussion.

Cheers,

Matthew Argall
Jacob Bortnik
Ryan McGranaghan
Viacheslav Sadykov

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What

AGU Town Hall

Title

The New Sun-to-Earth Machine Learning Exploratory Committee: Collecting the
National Science Foundation Communities for the Application of Machine
Learning to Space Physics

When

Friday, 11 December, 2020

1:30 -- 2:30 Eastern

Link

https://agu.confex.com/agu/fm20/meetingapp.cgi/Session/103176

Description

This Town Hall brings together thought-leaders from across the
solar-terrestrial spectrum to discuss topics in machine learning and data
science. The agenda consists of a distributed tutorial offering lightning
talks on the use, needs, and potential for machine learning within space
physics; NSF representatives from the Geospace Section and one of NSF’s 10
Big Ideas, Convergence, to provide insights into what is coming from NSF;
and a panel discussion for the community to raise critical questions, and
express opinions on the most compelling and valued aspects of machine
learning.

The lightning talk covers topics on machine learning in the space sciences,
including: 1) Techniques and Strengths; 2) Science Discovery; 3) Handling
and Visualizing Data; 4) Data Assimilation; 5) Space Weather Prediction; 6)
An Industry Perspective from NVIDIA; and 7) A Community Perspective from a
NASA/NSF DRIVE Center.

Scientists in Heliophysics with ties to the SHINE, GEM, and CEDAR
communities or with an interest in data science and machine learning are
encouraged to attend.

Lastly, we have prepared this short survey to help shape the panel
discussion and plan future efforts:

https://docs.google.com/forms/d/e/1FAIpQLSf3UYL_OQpIIQAUEcR74D42xPLib1np0fhR-4llwbQsmma6QA/viewform?usp=sf_link
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