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Kiri L. Wagstaff
Ph.D. in Computer Science, Cornell University, 2002
Intelligent Clustering with Instance-Level Constraints
M.S. in Computer Science, Cornell University, 2000
B.S. in Computer Science, University of Utah, 1997
M.S. in Geological Sciences, University of Southern California, 2008
Biogenicity Analysis of Stromatolite Structures
Master of Library and Information Science, San Jose State University, 2017
Automated Classification to Improve the Efficiency of Weeding Library Collections
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 2023
Private pilot's certificate (airplane single-engine land), 2016
Instrument rating, 2023
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 worked 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 (2003 to 2022).
My other roles at JPL include serving as a
tactical planner and uplink lead for the
Mars Exploration Rover Opportunity (2013 to 2017) and
the PDS Imaging Node
Technologist (2018 to 2022).
I've also conducted research and
taught classes in Computer Science
at Oregon State University.
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:
Library science papers:
Selected awards and honors:
- 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
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 volunteered in support of the
Monrovia Public Library and
Kids Building Things for several years.
- 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).
- 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!