Hannah Rae Kerner
PhD student at the School of Earth and Space Exploration at ASU (advisor: Dr. Jim Bell)
- first_name . middle_initial . last_name @ gmail
I am researching machine learning applications for planetary science.
My primary interest is in training models to recognize “novelty” or geologically interesting features in planetary data (images, spectra, etc.). The idea is to create artificially intelligent programs that can act like members of a science mission team to prioritize observations for review by human scientists to assist with tactical and strategic planning (e.g. deciding where to drive the rover and what surface features to investigate further), thus increasing the scientific return of exploration missions.
I also work on various machine learning tools that improve workflows for scientists and missions as well as doing the occasional neutron modeling.
I work on the following missions:
- Lunar Polar Hydrogen Mapper (LunaH-Map): This 6U CubeSat mission was recently selected by NASA's Science Mission Directorate to fly as a secondary payload on first Exploration Mission (EM-1) of the Space Launch System (SLS), scheduled to launch in September 2018. The mission is led by Arizona State University (PI: Dr. Craig Hardgrove) and I'm the flight software lead. LunaH-Map is being designed to fly a pair of neutron spectrometers to map hydrogen abundances at the South Pole of the Moon.
- Mars Science Laboratory (Curiosity): The research described in the above section is designed specifically for the Mastcam and Dynamic Albedo of Neutrons (DAN) science investigations onboard Curiosity.
- Mars Exploration Rover (Opportunity): I am a payload downlink lead for the Pancam instrument onboard Opportunity.
PublicationsKerner, H. R., Bell III, J. F., Ben Amor, H. (2017). Context-dependent image quality assessment of JPEG compressed Mars Science Laboratory Mastcam images using convolutional neural networks. Computers and Geosciences. (submitted)
Kerner, H., Kuntz, A., Ichnowski, J., North, K. (2015). Robotics and Autonomous Driving.
Conference Abstracts/PresentationsKerner, H. R., Bell III, J. F., Ben Amor, H. (2017). Context-dependent image quality assessment of JPEG compressed Mars Science Laboratory Mastcam Curiosity images using convolutional neural networks. American Geophysical Union (AGU) Fall Meeting 2017.
Kerner, H. R., Bell III, J. F., Ben Amor, H. (2017). Detecting and characterizing compression-related artifacts in Mars Science Laboratory Mastcam images. 48th Lunar and Planetary Science Conference.
Kerner, H., Hardgrove, C., Bell, J., Lazbin, I., Amzler, R., Babuscia, A., Burnham, Z., Christian, J., Colaprete, A., Deran, A., Drake, D., Dunham, D., Genova, A., Godber, A., Johnson, E., Lightholder, J., Nelson, D., Robinson, M., Starr, R., West, S., Williams (2016). The Lunar Polar Hydrogen Mapper (LunaH-Map) CubeSat Mission. 30th Annual AIAA/USU Conference on Small Satellites.
WritingsOur path to Mars needs to look beyond the launch , Houston Chronicle 2016
Space Technology Can Help Sustain Earth , Scientific American 2016
The Space Destination Debate is Getting Us Nowhere... Literally, Space.com 2015
What's The Point? The Real Reason Scientists Study Space, Space.com 2015
It's Not Them It's You: Why Top Tech Talent Isn't Going to the Satellite Industry, Via Satellite 2015
The Next Generation of Next-Generation Activities, Space News 2015
The B-Word, Planet Pulse 2015