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Machine Learning for Spacecraft at Europa: Enabling Faster Exploration in a High-Radiation Environment

Kiri L. Wagstaff
Senior Instructor
Oregon State University

Upcoming missions to remote destinations like Jupiter’s moon Europa will operate at extreme distances from the Earth where direct human oversight is impossible. The combination of extreme distance, limited lifetime due to high radiation, and limited data downlink creates an urgent need for reliable autonomous operations.

Machine learning can help by analyzing data for features of interest as it is collected. Data with positive detections can be marked for high priority downlink to Earth for mission planning. For Europa, such features include active icy plumes and unusual surface mineral deposits.

This talk describes data analysis and machine learning methods that can operate onboard to increase the rate of exploration and discovery. I will also describe how to assess algorithm radiation sensitivity to determine which ones are sufficiently robust for mission use.

Speaker Bio
Dr. Kiri L. Wagstaff is a Senior Instructor at OSU and a Principal Researcher in machine learning at NASA's Jet Propulsion Laboratory. Her research focuses on developing new machine learning methods for use onboard spacecraft and in data archives for planetary science, astronomy, cosmology, and more.

She earned a Ph.D. in Computer Science from Cornell University followed by an M.S. in Geological Sciences and a Master of Library and Information Science (MLIS). She is a Senior Member of AAAI and has served as a Tactical Uplink Lead (operational planning) for the Mars Opportunity rover. She is passionate about keeping machine learning relevant to real-world problems.

Wednesday, January 22 at 1:00pm to 2:00pm

Gleeson Hall (Chem Engr), 200
2115 SW Campus Way, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic




Electrical Engineering and Computer Science
Contact Name

Michael Slater

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