Mining Mars Targets from the Planetary Science Literature

Friday, June 23, 2017, 11:00 am - 12:00 pm PDTiCal
11th floor large conference room
This event is open to the public.
AI Seminar
Kiri Wagstaff, JPL

Every day, rovers on Mars send back data for new observation targets (e.g., rocks, soils, layers).  Some of these targets yield new discoveries that are published in the scientific literature.  Yet there is currently no accessible link between data (or targets) and their subsequent publications.  We are building the Mars Target Encyclopedia (MTE) to enable users to ask questions such as "What do we know about target Epworth?" and "What are all of the Mars targets that contain chlorine?"  We use information extraction and machine learning methods to mine the steadily growing body of scientific publications and extract compositional knowledge about Mars surface targets.  The MTE benefits Mars mission planners, planetary scientists, and the interested public by condensing relevant knowledge into a central resource in an accessible way.  More than just a literature search, the MTE allows us to ask new questions that previously could not be answered.

Bio: Dr. Kiri L. Wagstaff is a principal researcher in artificial intelligence and machine learning and a tactical uplink lead for the Mars rover Opportunity at the Jet Propulsion Laboratory.  Her research focuses on developing new machine learning and data analysis methods, particularly those that can be used for in situ analysis onboard spacecraft such as orbiters, landers, and rovers.  She holds a Ph.D. in Computer Science from Cornell University followed by an M.S. in Geological Sciences from the University of Southern California and an MLIS from San Jose State University.  She received a 2008 Lew Allen Award for Excellence in Research for work on the sensitivity of machine learning methods to high-radiation space environments and a 2012 NASA Exceptional Technology Achievement Award for work on transient detection methods in radio astronomy data.  She is passionate about keeping machine learning relevant to real-world problems.


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