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SURF: Announcements of Opportunity

Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.

Specific GROWTH projects being offerred for summer 2019 can be found here.

Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.

Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor.

Announcements for external summer programs are listed here.

Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.

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Project:  Machine Learning and Data Analysis in Support of Europa Exploration
Disciplines:  Computer Science, Planetary Science
Mentor:  Kiri Wagstaff, (JPL), Kiri.L.Wagstaff@jpl.nasa.gov, Phone: (818) 393-6393
Mentor URL:  http://www.wkiri.com/  (opens in new window)
Background:  The Europa Clipper mission will launch in 2023 and arrive at Jupiter's moon Europa several years later. It will conduct ~40 flybys of Europa and collect data with nine different instruments, including visible and thermal imagers, a mass spectrometer, a radar sounder, a magnetometer, and more. Each flyby will reveal new discoveries about Europa. Key science goals include a determination of the thickness of the moon's ice shell, the nature and composition of the subsurface ocean, and the detection of any current or past eruption or plume activity. Because the spacecraft must operate under strict downlink volume constraints, we have developed methods for analyzing data as it is collected to assess its science value and therefore its priority for downlink. The goal is to quickly transmit the most informative and scientifically valuable data so that mission planners on the Earth can make the best decisions about what data to collect on the next flyby.
Description:  In this project, you will investigate the application of data analysis and machine learning methods to the kinds of data that will be collected by Europa Clipper. Examples include images collected by the Europa Imaging System (EIS), thermal data from the Europa THermal Emission Imaging System (E-THEMIS), hyperspectral images from the Mapping Imaging Spectrometer for Europa (MISE), or ion and electron counts from the Plasma Instrument for Magnetic Sounding (PIMS). You will implement and assess the performance of data analysis methods that aim to detect events of interest, such as hot spots that could indicate subsurface upwelling or plumes that could indicate geysers or cryovolcanic activity. See Reference [2] for more information and examples. You will also investigate the use of outlier or anomaly detection methods to detect the unexpected, such as unusual surface mineralogy or terrain in images. See Reference [3] for one possible approach.
----- Our goal is to publish the result of this work, and you would contribute to the paper as a co-author.
----- This project requires strong software engineering skills (e.g., organizing class hierarchies, modular code, usability, documentation in terms of user guide and in-line code comments), particularly in Python.
-----
References:

1. Information about the Europa Clipper mission:
https://meetingorganizer.copernicus.org/EPSC2018/EPSC2018-528-2.pdf

2. "Onboard Detection of Thermal Anomalies for Europa Clipper" by
Gary Doran, Ashley G. Davies, Kiri L. Wagstaff, Saadat Anwar, Diana
L. Blaney, Steve Chien, Phil Christensen, and Serina Diniega.
European Planetary Science Congress, Abstract #528, September 2018.
https://meetingorganizer.copernicus.org/EPSC2018/EPSC2018-528-2.pdf

3. "Interpretable Discovery in Large Image Data Sets" by Kiri
L. Wagstaff and Jake Lee. Proceedings of the Workshop on Human
Interpretability in Machine Learning (WHI), p. 107-113, 2018.
https://jakehlee.github.io/interp-img-disc.html
References:  https://www.jpl.nasa.gov/missions/europa-clipper/
Student Requirements:  Proficiency in Python; excellent software engineering skills; experience with Linux/Unix and git version control; ability to communicate effectively orally and in writing. Coursework in Data Structures, Software Engineering, and Machine Learning.
Desired: Coursework/experience with Computer Vision and Statistics.
Location / Safety:  Project building and/or room locations: . Student will need special safety training: Yes.
Programs:  This AO can be done under the following programs:

  Program    Available To
       SURF    both Caltech and non-Caltech students 

Click on a program name for program info and application requirements.


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