CEDAR email: ML-GEM Resource Group Sessions for the 2024 GEM Summer Workshop

[Connor, Hyunju K. (GSFC-6730)]

Dear CEDAR members,



We invite you to the Machine Learning based Geospace Environment Modeling (ML-GEM) sessions at the 2024 GEM summer workshop held in Fort Collins, Colorado during Jun 23-28.  ML-GEM is a new resource group selected by the GEM Steering Committee, with two primary goals: advancing system-of-systems science in Sun-Earth interaction from a data-driven perspective and developing an ML-based Geospace Environment Modeling by integrating community-wide ML efforts.


ML-GEM chairs have scheduled 4 hybrid session. The meeting schedule and link will be announced later at https://gem.epss.ucla.edu/mediawiki/index.php/RG:_Machine_Learning. If you are interested in giving a talk in these sessions, please submit your talk at the following website: https://forms.gle/fmsU2eeBFFwD4tDQ8.


1. ML-GEM stand-alone session (10:30am - 12:00pm on 06/27 Thursday) :  All ML efforts across the GEM research areas are invited.


2. ML-GEM joint session with the Inner Magnetosphere Focus Groups (3:30 - 5:00pm on 06/27 Thursday) : ML efforts particularly in the inner magnetosphere research area are invited.


3. ML-GEM discussion session (10:30am - 12:00pm on 06/28 Friday) : We will showcase currently available AI models in the heliophysics community and discuss how to unify them into a single AI modeling framework for understanding and predicting the geospace environment.  Please submit a 2-page summary of your AI model at https://docs.google.com/presentation/d/1qzgn880hCaDHLkEhL6GUEcEI3m5SJjQK/edit?usp=drive_link&ouid=115923735998882159942&rtpof=true&sd=true


4. ML-GEM tutorial session (1:30 - 3:00pm on 06/28 Friday):  A hands-on tutorial on the Long Short-Term Memory (LSTM) technique that models time-series data. This tutorial will use the LSTM model and the SuperMAG geomagnetic field data, published in Blandin et al. (2022; https://doi.org/10.3389/fspas.2022.846291).


Please note that we intentionally schedule the ML-GEM tutorial session on Friday afternoon to minimize overlap with other sessions, ensuring scientists at any career levels can join and learn the new ML techniques without missing their sessions. If you are interested in the tutorial session, please plan your travel accordingly.


Thank you very much and we look forward to seeing you at the ML-GEM,

Hyunju Connor, Matthew Argall, Xiangning Chu, Bashi Ferdousi, and Valluri Sai Gowtam.

Note - If you receive this email on nights and/or weekends, that doesn't mean I expect you to read it or reply at that time. Please read & reply when you can during whatever your working hours happen to be.

---------------------------------------------------------------
Hyunju Kim Connor (she/her/hers)
Research Astrophysicist
Geospace Physics Laboratory, Code 673
NASA Goddard Space Flight Center
Rm. 236, Bldg. 21
8800 Greenbelt RD, Greenbelt, MD 20771, USA
Email     : Hyunju.k.connor at nasa.gov<mailto:Hyunju.k.connor at nasa.gov>
Tel.         : 301-286-7417
---------------------------------------------------------------

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mailman.ucar.edu/pipermail/cedar_email/attachments/20240607/4be2464d/attachment.htm>