Purpose

Building connections to advance applications

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 science and space physics have often been fields defined by sparse data concerns and extreme environments. These aspects provide a uniquely challenging, and rewarding field to apply data science methods.

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

2024 Seminar Series

4th June 2024

Tuesday at 9 AM US Pacific

Michael Holland
Brigham Young University

This Crater Does Not Exist: How Synthetically Created Craters Can Help Us Understand Crater Formation and Key Crater Features.

Andreas Bechtold
Austrian Academy of Sciences and University of Vienna

A simulation-based deep learning training approach for autonomous detection of shatter cones in Mars rover images.

Previous seminars with abstracts can be found on YouTube.

Join

To receive invitations to our monthly series or to recommend speakers, please fill out our community form.

Organizers

Indhu Varatharajan

Stony Brook University

Andy Smith

University College London

Past Organizers

Abigail Azari

Space Sciences Lab, University of California Berkeley

Caitriona Jackman

Dublin Institute for Advanced Studies