Kiri L. Wagstaff

My background is in Computer Science, Planetary Science, and Geology. I am most interested in problems that lie at the interfaces between these fields, such as automated methods (artificial intelligence, machine learning) to investigate science questions using planetary data (orbital and in situ).

I work at the Jet Propulsion Laboratory in Pasadena, CA, as a researcher in the Machine Learning and Instrument Autonomy Group, investigating ways that machine learning can be used to increase the autonomy of space missions. From 2013 to 2017, I also served as a tactical planner and uplink lead for the Mars Exploration Rover Opportunity. Since 2018, I have also served as the PDS Imaging Node Technologist.

I am also an Associate Research Professor at Oregon State University, where I have the pleasure of teaching classes in Computer Science and doing research on competency-aware machine learning.

My research projects at JPL have included:

• Onboard science for Europa Clipper: Developing and testing methods to quickly analyze data as it is collected during a flyby of Europa to assign high downlink priorities to the most scientifically valuable observations and to enable cross-instrument collaboration
• Mars Target Encyclopedia: Information extraction from scientific publications for planetary science
• V-FASTR: Efficient machine learning to detect transient radio phenomena (e.g., pulsars and Fast Radio Bursts) in real time
• Collaborative machine learning for sensor networks
• Automatic landmark identification and change detection in Mars orbital images (dark slope streaks, dust devil tracks, etc.)
• Analyzing the sensitivity of machine learning algorithms to high-radiation environments
• Predicting county-level crop yield from Earth orbital images
• Modeling user preferences for sets, rather than individual items (like music playlists or rover image downlink sets)
• Modeling flight software with state charts and using automatic code generation to convert them into C/C++ (for implementation) or Promela (for model checking)
• Tracking the north polar ice caps (water and CO₂) on Mars

News and upcoming events:

• Press release (Oct. 1, 2020): AI Is Helping Scientists Discover Fresh Craters on Mars
  • NPR KPCC Interview (Oct. 9, 2020; 5 min)
  • Machine Learning Software is Now Doing the Exhausting Task of Counting Craters On Mars
  • NASA Unveils a Cluster of Mars Craters Discovered by AI
  • Scientists Are Finding a Ton of New and Mysterious Craters on Mars
  • Working with NASA, Brown team teaches computers to look for new Mars craters
• The video for my controversial ICML 2012 talk is no longer available (lost in a server crash). However, you can read the original paper: Machine Learning that Matters (pdf, 6 pages, 234K) and see the slides from a subsequent invited AAAI talk: Challenges for Machine Learning Impact on the Real World (1.6M).
• Recently published or posted:
  • The Mars Target Encyclopedia Now Includes Mars Pathfinder and Mars Phoenix Targets. (Poster)
    Kiri L. Wagstaff, Raymond Francis, Matthew Golombek, Steven Lu, Ellen Riloff, Leslie Tamppari, and Thomas C. Stein.
    52nd Lunar and Planetary Science Conference, Abstract #1278, March 2021.
    • What about Yogi? / Mars mission targets abound / You can find them here
    • The MTE database is now available at the PDS: Mars Target Encyclopedia
  • Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances
    (IAAI Deployed Application Award).
    Kiri Wagstaff, Steven Lu, Emily Dunkel, Kevin Grimes, Brandon Zhao, Jesse Cai, Shoshanna B. Cole, Gary Doran, Raymond Francis, Jake Lee, and Lukas Mandrake.
    Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence, 2021.
    • We report on updates to Mars image classifiers deployed at the NASA Planetary Data System, for orbital and rover images, and lessons learned that inform ongoing activities to build new classifiers for additional missions.
• All publications

• Automated Classification to Improve the Efficiency of Weeding Library Collections.
  Kiri L. Wagstaff and Geoffrey Z. Liu.
  Journal of Academic Librarianship, 44(2), p. 238-247, 2018.
  • We evaluated several machine learning classifiers in terms of their ability to predict which books are most likely to be weeded from a collection. We applied this method to a collection of more than 80,000 items from an academic library and found statistically significant agreement (p = 0.001) between classifier and librarian decisions.
• Marginalia in the digital age: Are digital reading devices meeting the needs of today's readers?
  Melanie Ramdarshan Bold and Kiri L. Wagstaff.
  Library & Information Science Research, 39(1), 16-22, 2017.
  • We surveyed readers to find out about their attitudes toward marginalia, and whether and how often they indulged in it themselves. We also investigated whether marginalia translates into electronic books and which features are most desired by users of e-readers.
• The Early History of the Monrovia Library, my term paper for LIBR 280 (pdf, 16 pages, 1.0M)
• The Evolution of Marginalia, my term paper for LIBR 200 (pdf, 14 pages, 1.1M)

• 2020 NASA Exceptional Technology Achievement Medal for "inventing dynamic landmarking [to detect surface features from orbital images] leading to novel onboard mission capabilities and infusion into the Planetary Data System"
• 2019 Senior Member of AAAI
• 2017 Calvert N. Ellis Memorial Lectureship from Juniata College in Huntingdon, PA
• I won the 2017 National Adult Spelling Bee!
• I was elected to the AAAI Executive Council for 2015-2018.
• I was promoted to Principal at JPL in January 2015.
• 2014 NASA Group Achievement Award (Mars Exploration Rover Science and Operations Team)
• 2014 NASA Group Achievement Award (IPEX/CP-8 CubeSat Flight Team)
• 2012 NASA Exceptional Technology Achievement Medal for "the development of adaptive data processing techniques for identification of time-varying sources with next-generation radio arrays"
• 2012 AAAI Outstanding Program Committee Member Award (one of four people chosen)
• 2012 Young Alumni Par Excellence Award from the University of Utah
• 2008 Lew Allen Award for Excellence in Research for "advancing the performance and application of machine learning methods to onboard Earth science missions and spacecraft engineering."

• I volunteer in support of the Monrovia Public Library and Kids Building Things.
• I traveled to Guatemala to volunteer with Librarians Without Borders in April 2018.
• I served as Editor in Chief of AI Matters, the SIGAI Newsletter, from April 2014 to June 2015.
• I taught Space Camp to middle school students in South Korea in the summer of 2014.
• I was selected as one of 1058 candidates for Mars One's second round of astronaut selections (Dec. 30, 2013), which were then whittled down to 705 candidates (May 5, 2014). However, I was not selected for round 3 (100 candidates).
  • The 1-minute video for my Mars One application is available. I'd love to hear what you think!
  • Read this great writeup in The Times-Independent (my hometown newspaper) (Aug. 22, 2013)
  • I served as part of Crew 89 at the Mars Desert Research Station, from January 22 to February 7, 2010.
    You can read our blog to find out what we did, see pictures, and watch videos!