Oregon Master
Naturalist (in progress)
I am an avid learner, researcher, and educator.
I spent 19 years at the Jet
Propulsion Laboratory in Pasadena, CA, as a Principal Researcher in the
Machine Learning and Instrument
Autonomy Group (2003 to 2022). At JPL, I focused on investigating ways
that machine learning can be used to enable scientific discoveries
and increase the autonomy of space missions. It has been a richly rewarding,
exciting position working with stellar colleagues from planetary science,
radio astronomy, cosmology, agriculture, library science, and other fields. In
addition, I had the unusual chance to serve as a
tactical planner and uplink lead for the Opportunity
Mars Exploration Rover (2013 to 2017) and as the
PDS Imaging Node
Technologist (2018 to 2022).
In 2022 I made the decision to retire from JPL and identify additional
areas in which I could have impact. From 2019 to 2023 I
taught courses at Oregon
State University. I am now serving as
a AAAS
Congressional Fellow in Artificial Intelligence for
Senator Mark Kelly in Washington D.C.
I am passionate about keeping machine learning efforts relevant to our
society's needs.
My research projects 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 extracted from scientific
publications for planetary science
(MTE GitHub)
- V-FASTR: Efficient machine learning to detect transient radio phenomena (e.g., pulsars and Fast Radio Bursts) in real time
- Collaborative machine learning for volcano 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:
-
I will give a talk at OSU on Oct. 25 on What I Learned Serving as an AI Expert in the U.S. Congress.
-
Senators Kelly and Rounds introduced
the Consumers
LEARN AI Act on July 30, 2024. I proposed and developed this
bill with an amazing team for many months. The goal is to help
people make informed decisions about when and where to use AI in
their lives. I'm all about education and empowerment!
- I
was interviewed
by Nature in May 2024 about my work in Congress as a AAAS Fellow.
- I served as Chair of
the new
ICML 2024 Position Paper track. You
can browse
these fascinating papers and learn more about the track in
this ICML
behind the scenes chat about position papers.
- I gave a 10-minute presentation on AI questions before the
U.S. Congress at AAAI in February 2024 (my part starts at 27:50).
- I was chosen as a member of the
AAAS Science and Technology Policy Fellowship Rapid Response AI
Cohort (2023-2024).
- I was elected a
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2023
for "significant contributions to deployed AI systems in NASA ground and spacecraft
operations, AI education, and clustering algorithms."
- Andy Okamoto is this
year's recipient of the Kiri
L. Wagstaff AI/ML scholarship at the University of
Utah. Congratulations!
- Recommended:
Machine Learning that Matters (pdf, 6 pages, 234K) and
the slides from a subsequent invited AAAI talk:
Challenges for Machine Learning Impact on the Real World (1.6M).
- Interview with the How to Do Grad School podcast:
Why We Learn, How to Write, & Fostering Collaboration (August 2021)
- Recently published or posted:
- Machine Learning for Healthcare that Matters: Reorienting
from Technical Novelty to Equitable Impact.
Aparna Balagopalan, Ioana Baldini, Leo Anthony Celi, Judy
Gichoya, Liam G. McCoy, Tristan Naumann, Uri Shalit, Mihaela
van der Schaar, and Kiri L. Wagstaff.
PLOS Digital Health, DOI 10.1371/journal.pdig.0000474, 2024.
- A compendium of obstacles and Ideas for how to move
from doing machine learning _on_ healthcare data to
using machine learning _for_ healthcare, to achieve real
impact.
- All publications