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 am a researcher at the Jet Propulsion Laboratory in Pasadena, CA. I am a member of JPL's Machine Learning and Instrument Autonomy Group, investigating ways that machine learning can be used to increase the autonomy of space missions. I have also taught classes in Computer Science at Cal State L.A. and Oregon State University. My projects at JPL have included:

• Efficient machine learning to detect transient radio phenomena (e.g., pulsars) 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:

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• The video for my controversial ICML 2012 talk is now available: Machine Learning that Matters.
  You can also 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).
• Recent talks:
  • Interactive Discovery of New Phenomena in Martian Point Spectra and Hyperspectral Data Sets (pdf slides, 4M) at AGU on Dec. 3, 2012
• Recently published or posted:
  • Guiding Scientific Discovery with Explanations using DEMUD.
    Kiri L. Wagstaff, Nina L. Lanza, David R. Thompson, Thomas G. Dietterich, and Martha S. Gilmore.
    Proceedings of the Twenty-Seventh Conference on Artificial Intelligence (AAAI-13), 2013.
    • DEMUD is an algorithm for quickly finding interesting novelties or anomalies in large data sets. Uniquely, it also provides explanations for why each item is considered interesting. We report on experiments with hyperspectral data from Mars orbit and the Martian surface, finding that DEMUD indeed discovers items of scientific interest, and that the explanations are useful: with them, a scientist's ability to classify spectra doubled.
  • The Evolution of Marginalia, my term paper for LIBR 200 (pdf, 14 pages, 1.1M)
• Recent awards:
  • 2012 NASA Exceptional Technology Achievement Medal
  • 2012 AAAI Outstanding Program Committee Member Award (one of four people chosen)
  • 2012 Young Alumni Par Excellence Award from the University of Utah
• Current activities:
  • I am serving on the organizing committee for EAAI 2013, as an area chair for AAAI 2013, and on the program committee for ICML 2013.
  • I am an action editor for the Machine Learning journal and co-editing a special issue on Machine Learning for Science and Society.
  • I volunteer in support of the Monrovia Public Library, Kids Building Things, and the UU LGBT Youth Task Force to provide LGBT youth with safe, supportive, social environments.
• 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!
• I was selected to receive the 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."