Machine Learning in Geospace
- 1 VGEM 2021
- 2 VGEM 2020
We are back for a second year organizing the Machine Learning in Geospace session at the VGEM 2021 Workshop. This year, we have two sessions. One will be filled with invited and submitted talks and the other will focus on organizing a Kaggle Competition within the community to address a large, inter-disciplinary science problem using machine learning and data science techniques. See our Schedule Overview here and our detailed schedule below.
The workshop begins on July 25 and ends on July 30.
|Student Day||July 25||Sunday||1:00 - 5:00 PM|
|VGEM Workshop||July 26 - 30||Monday - Friday||11:00 AM - 4:30 PM + Posters|
|ML Session I||July 28||Wednesday||3:00 - 4:00 PM|
|ML Session II||July 30||Friday||1:00 - 2:30 PM|
Session I: Submitted Talks
|10 min||Matthew Argall||Session overview, decadal planning|
|10 min||Breakout Rooms||Ice Breaker|
|10 min||Ayris Narock||Ethical AI|
|6 min||Matthew Blandin||Prediction of Geomagnetic Field Disturbances across Alaska using Machine Learned LSTM Neural Networks|
|6 min||Sheng Huang||Recurrent neural network implementation of modelling global plasmaspheric density|
|6 min||Michael Coughlan||Using OMNI and SuperMag data to determine the risk of db/dt threshold exceedance with Long-Short Term Memory and Convolutional Neural Networks.|
|6 min||Chris Bard||Reconstructing MHD with Neural Networks|
|6 min||Anthony Saikin||NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux|
|6 min||Eric Donovan||Applying ML to the THEMIS-ASI data set|
|30 min||Open Discussion|
Session II: Kaggle Competition
|5 min||Matthew Argall||Session overview|
|15 min||Addison Howard||How to Design a Successful Kaggle Competition|
|5 min||Manoj Nair||NOAA's DataDriven MagNet Chellenge|
|5 min||Barbara Thompson||AI-Ready Datasets|
|5 min||Raphael Attie||Data classification tools|
|5 min||Wendy Carande||Getting Started with ML: Common Problems|
|20 min||Breakout Rooms||Designing the competition.|
* What problems can be addressed with an ML Competition?
* What missions or datasets would be useful?
* What resources are required to prepare for a successful competition?
* What evaluation criteria will lead to a fair and meaningful winner?
* Get the discussion started early by contributing to our Jam Board
|15 min||Andrés Muñoz-Jaramillo
|"What & How" A recap of the breakout sessions|
|20 min||Speakers & Panelists||Open Discussion on Kaggle Competition|
Heliophysics Decadal Survey White Papers
Members of the Heliophysics Community are coordinating and organizing white papers on Machine Learning and Data Science for the Heliophysics Decadal Survey in order to increase their visibility and impact. Anyone interested in leading, contributing to, or supporting a white paper, please reach out to Matthew Argall email@example.com for more details.
|Student Day||Monday, July 20th, 2020 is Student Day|
|GEM Workshop||Tuesday - Thursday, July 21st - 23rd|
Machine Learning Session
Thursday, July 23, 3:00 - 4:30pm Eastern
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.
|5 min||Opening remarks||Matthew Argall|
|15 min||Introduction to ML: "What is Possible?"||Jacob Bortnik|
(See Table 3)
|Application(s): ML in GEM with relevance to SHINE/CEDAR||Enrico Camporeale|
|Application of Machine Learning to GNSS-Related Remote Sensing Tasks||Leo Liu|
|Predicting Solar Eruptions: What has ML done for us?||Sophie Murray|
|40 min||Open Panel Discussion||See Table 2|
|SHINE||Wendy Carande||Y||Pairing science problems with ML tools|
|SHINE||Barbara Thompson||NASA, Policy|
|GEM||Simon Wing||Information Theory|
|GEM||Xiangning Chu||Y||Global Reconstruction|
Industry to academia
|Planetary||Abby Azari||Y||Student tutorial|
Physics Informed ML
|Industry||Nick Bunch||Y||Academia to industry|
|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|