People
- Tara Estlin
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Tara Estlin is a senior member of the Artificial Intelligence Group at
the Jet Propulsion Laboratory (JPL). She has over 10 years of
experience in developing spacecraft autonomy software. A primary goal
of these efforts is to support onboard sequencing and opportunistic
science handling for future rover missions. She is currently leading
the AEGIS Project, which is providing new automated targeting
technology for the MER rovers. Dr. Estlin is also presently a rover
driver for the Mars Exploration Rover (MER) mission where she is
responsible for sequencing drive and arm deployment commands for the
MER Spirit and Opportunity rovers. She holds a B.S. in computer
science from Tulane University and a Ph.D. in computer science from
the University of Texas at Austin.
- Ben Bornstein
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Ben Bornstein is a senior member of the Machine Learning and
Instrument Autonomy group at the Jet Propulsion Laboratory (JPL). He
has over 10 years experience in developing onboard autonomy software
for spacecraft and instruments. Ben led the instrument flight
software and onboard autonomy software efforts for the Vehicle Cabin
Atmosphere Monitor (VCAM), a GC/MS instrument currently monitoring the
crew cabin atmosphere onboard the International Space Station (ISS).
He led the development of the onboard pattern recognition software for
JPL’s most recent Electronic Nose (ENose). Ben is currently a
technical lead for the AEGIS Project, which is providing new automated
targeting technology for the MER rovers. He helped develop the MER
cloud and dust-devil detectors, an atmospheric science autonomy
technology uplinked to the MER rovers in 2006. Ben enjoys bringing
machine learning techniques and considerable hacking (programming)
skills to bear to solve problems in geology, remote sensing,
chemistry, bioinformatics, and systems biology. Ben received a
B.Sc. in Computer Science from the University of Minnesota Duluth in
1999.
- Dan Gaines
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Dr. Daniel Gaines is a senior member of the Artificial Intelligence
Group at the Jet Propulsion Laboratory (JPL). His research interests
are in integrated planning and execution and in machine learning to
improve planning. His work at JPL is primarily focused on planning and
execution techniques for planetary exploration rovers. Dr. Gaines
received a Ph.D. in Computer Science from the University of Illinois
at Urbana-Champaign. Before coming to JPL, Dr. Gaines was an Assistant
Professor of Computer Science at Vanderbilt University.
- Robert C. Anderson
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Dr. Robert C. Anderson is a senior member of the Geophysics and
Planetary Geosciences group at the Jet Propulsion Laboratory (JPL).
Dr. Anderson attended Old Dominion University in Norfolk, Virginia,
where he received his Bachelor of Science degree in geology in
1979. In 1985, he received a Master of Science from Old Dominion
University in geology with an emphasis on structural geology and
mapping tectonic features surrounding the Tharsis region of Mars. In
1995, he received a Doctor of Philosophy from the University of
Pittsburgh in geology with an emphasis on visible and near infrared
remote sensing. Dr. Anderson worked on the Mars Pathfinder Project as
science support for the Mineralogy and Geochemistry Science Operations
Group. Dr. Anderson was the Investigation Scientist for the Rock
Abrasion Tool (RAT) and science support for Mission Operations on the
Mars Exploration Rover (MER) mission. Currently, Dr. Anderson is an
Investigation Scientist for the Mars Science Laboratory (MSL) Sample
Acquisition / Sample Handling (SA/SPaH) subsystem.
- David R. Thompson
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Dr. David Thompson is a researcher in the Machine Learning and
Instrument Autonomy group at the Jet Propulsion Laboratory (JPL). He
received an M.Sc. degree in informatics from the University of
Edinburgh, Edinburgh, U.K. and the Ph.D. degree in robotics from the
Carnegie Mellon Robotics Institute in Pittsburgh, PA. David has
published over 30 articles on autonomous data analysis in a wide range
of fields including planetary geology, oceanography, and radio
astronomy. He currently leads several research projects applying
machine learning and pattern recognition for remote sensing and
autonomous planetary science. His algorithms have guided robot
sciencecraft in North America, South America, the Atlantic Ocean, the
Pacific Ocean, and on Mars.
- Charles de Granville
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Charles de Granville is currently bringing his software development
expertise to bear developing credit risk analytics software for MSCI
in Norman, OK. Prior to joining MSCI, Charles was a research software
engineer in the Machine Learning and Instrument Autonomy group at the
Jet Propulsion Laboratory (JPL). He graduated from the University of
Oklahoma with a B.S. and M.S. in Computer Science. His research
interests include the development of machine learning algorithms that
facilitate robot control for space applications. More specifically, in
the context of robot manipulation he has worked on algorithms that
learn representations for how objects can be grasped.
- Michael Burl
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Michael C. Burl is a Principal Member of the Technical Staff at the
Jet Propulsion Laboratory (JPL) in the Machine Learning and Instrument
Autonomy Group. He earned his Ph.D. in Electrical Engineering from
Caltech in 1997 and is well-known for his work in applied machine
learning, computer vision, data mining, and signal processing. Among
his accomplishments, he co-developed (with Pietro Perona and later
colleagues) the constellation model for recognition of visual object
classes, architected large-scale learning-based systems for locating
and cataloging geological features in planetary datasets. He
developed the segmentation algorithm ("Rockster") being used by
AEGIS. He is currently the principal investigator on a task to develop
visual intelligence algorithms for the DARPA Mind's Eye program (joint
work with Russell Knight). Previously, he served as an Assistant
Professor in the Department of Computer Science at the University of
Colorado (Boulder), a Senior Research Scientist at Evolution Robotics,
and an Associate Staff member in the Battlefield Surveillance Group at
MIT Lincoln Laboratory.
- Rebecca Castaño
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Dr. Rebecca Castaño, is currently the Assistant Section Manager
for the Instrument Software and Science Data Systems Section at the
Jet Propulsion Laboratory (JPL) and the OCO-2 Science Algorithm team
lead. She received her Ph.D. in Electrical Engineering from the
University of Illinois with her dissertion in the area of computer
vision. Dr. Castaño has been advancing the state of the art in
onboard science analysis methods for the past twelve years and has
been lead author on numerous publications in this field. From
2002-2007, Dr. Castaño was the Supervisor of the Machine
Learning Systems Group at JPL. Her research interests include machine
learning, computer vision and pattern recognition.
- Michele Judd
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Michele Judd is the Managing Director of the Keck Institute for Space
Studies (KISS), a joint institute of the Jet Propulsion Laboratory
(JPL) and Caltech. Prior to joining KISS, Michele was in JPL's
Science Division where she worked on several special projects to
improve the environment for researchers and helped manage the OASIS,
AEGIS, ServoGrid and QuakeSim research teams. In 2009, Asteroid 185641
was renamed "Judd" in honor of her work at JPL. Before coming to JPL,
she managed her own consulting business and had a twelve-year career
at Mobil Oil, where she held positions in the areas of strategic
planning, quality management, engineering and field operations.
