Purpose

Building connections

Machine learning (ML), and other large-data analysis techniques, have become essential tools for analyzing the ever-increasing sizes of scientific data returned from planetary missions. In addition, ML methods have demonstrated potential to assist in mission planning and operations. Planetary space physics (or the study of plasmas around planets) has often been a field defined by sparse data concerns and extreme environments. These aspects provide a uniquely challenging, and rewarding field to apply ML methods.

This seminar series aims to bring together researchers in planetary science, space physics, machine learning, and other domain applications of ML methods to advance applications of large-data methods. We welcome presentations from a broad array of fields including: Earth based and planetary science applications, educational efforts, and basic ML research.

Upcoming Seminar

October 27, 2020
9 AM Pacific / 12 PM Eastern / 4 PM Dublin

Kiri Wagstaff
NASA Jet Propulsion Laboratory /
Oregon State University

Machine Learning for Spacecraft at Europa: Enabling In-Situ Discoveries to Maximize Science Return [2019 paper], [2020 paper]

Matthew Argall
University of New Hampshire

The MMS SITL Ground Loop: Automating the Burst Selection Process [paper], [book chapter]

Current seminar series schedule with abstracts.

Organizers

Abigail Azari

Post-Doctoral Scholar, Space Sciences Lab, University of California Berkeley

Caitriona Jackman

Honorary Professor, Dublin Institute for Advanced Studies

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