Private pilot's license (single-engine land), 2016
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.
I have two roles at JPL:
I have also taught classes in Computer Science at Cal State L.A.
and Oregon State University.
My research projects at JPL have included:
- Mars Target Encyclopedia: Information extraction from scientific publications for planetary science
- V-FASTR: 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:
- 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:
- Library science papers:
- 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)
- Selected awards and honors:
- 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
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
- 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."
- Extracurricular activities: