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. After receiving my Ph.D. in Computer Science from Cornell University in 2002, I worked for a year in the JHU Applied Physics Lab's Space Department. My two major projects dealt with space weather prediction and the fault protection system for the MESSENGER spacecraft. I am now 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 at Cal State L.A. in the Computer Science Department. 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:

Newly published:
- Offline and Online Classification of Simulated VAST Transients (pdf, 18 pages, 3M).
  Umaa Rebbapragada, Kitty Lo, Kiri L. Wagstaff, Colorado Reed, and Tara Murphy.
  VAST Memo 5, 2012.
  - A report on the performance of different strategies for classifying variable and transient sources in radio astronomy data, in support of the VAST investigation using the ASKAP array. We explored both offline (archival) and online (realtime) classification methods.
Current activities:
- I am serving on the organizing committee for EAAI 2012 and on the program committee for AAAI 2012.
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."