Anantha Ravi Kiran
Steven (You) Lu
The Machine Learning and Instrument Autonomy (MLIA) Group
creates software solutions
to hard problems requiring data mining, knowledge discovery, pattern
recognition, and automated classification and clustering. The
underlying emphasis is on building systems based on learning
algorithms. We conduct basic research as well as develop applications
leading to one-of-a-kind proof of concept systems. Our focus is on the
automated analysis of scientific data generated by NASA and JPL
instruments, on the development of technologies for adaptive systems,
and on enabling technologies for autonomous spacecraft.
- Now hiring:
Come help us create machine learning systems for science and
- Machine Learning Research Data Scientist (Ph.D.):
Contact MLfirstname.lastname@example.org with your CV and
a statement of interest
Citizen science with COSMIC (Content-based Onboard
Summarization to Monitor Infrequent Change):
We are building an ambitious autonomous system to find and track
changing events on a planetary scale around Mars.
You can help by studying fascinating Mars images and labeling points of
interest through this easy-to-use interface:
Umaa Rebbapragada was co-author on two papers related to the
first detection of gravitation waves at LIGO. She is one of over 1500
"Localization and broadband follow-up of the gravitational wave
transient GW150914" by B. P. Abbott et al. of the LIGO
collaboration. She is also a co-author on a paper released
by the intermediate Palomar Transient Factory (iPTF) titled
"iPTF Search for an Optical Counterpart to Gravitational Wave Trigger."
Both papers document how iPTF was involved in the follow-up effort to
analyze regions of the sky containing GW150914. iPTF did not find any
follow up candidates associated with the gravitational wave trigger.
However, the papers document the end-to-end discovery process in
following up a gravitational wave detection. JPL-developed software for
transient vetting enables the discovery of supernovae in iPTF imagery.
The JPL team that created the "real-bogus" transient vetting software
consists of Umaa Rebbapragada, Brian Bue, and Gary Doran.
The intermediate Palomar Transient Factory (iPTF) made a first
significant discovery of a young astronomical transient (currently
hypothesized to be a very early detection of a Type Ia supernova) via
the real-bogus transient vetting that was deployed to the image
subtraction pipeline hosted by the the Infrared Processing and
Analysis Center (IPAC) for iPTF. This discovery was reported in an
Discovery of a Young Transient in a Tidal Tail of NGC 5221."
Umaa Rebbapragada, Brian Bue, and Gary Doran are listed as co-authors
to acknowledge work on the JPL-developed "real-bogus" transient
vetting software developed for the IPAC pipeline. This discovery is
worthy of note because the original image subtraction pipeline (in
operation at the National Energy Research Scientific Computing Center)
did not find this very young supernova candidate.
- Lukas Mandrake received the Runner-Up award for the 2015 NASA Software
of the Year competition for the Data Optimization via Genetic
Ordering (DOGO) System, which automatically ranks data by its
estimated quality and has been adopted by the OCO-2 mission. You
can learn more about DOGO by viewing
SOY DOGO presentation and
Tech Brief on DOGO.