Master of Library and Information Science, San Jose State University, in progress
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 have two roles at JPL: I am a member of the Machine Learning and Instrument
Autonomy Group, investigating ways that machine learning can be
used to increase the autonomy of space missions,
and I am a TAP/SIE (Tactical Activity Planner /
Sequence Integration Engineer) for the
Mars Exploration Rover Opportunity.
I have also taught classes in Computer Science at Cal State L.A.
and Oregon State University.
My research projects at JPL have included:
News and upcoming events:
- I was selected as one of 1058 candidates for
Mars One's second round of astronaut selections (Dec. 30, 2013)
- Wired magazine interviewed us, then published an article about our TextureCam rover camera:
- The AGU issued a press release about our TextureCam rover camera:
- 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).
- I will give a talk titled "Automated data prioritization and explanation for scientific discovery of Martian minerals, exoplanets, and more" at ISI on January 31, 2014.
- Recently published or posted:
- 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 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."