Science Serving Data Science

Areas of Expertise

Current Projects

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  Mars Target Encyclopedia (MTE)

  The Mars Target Encyclopedia (MTE) aims to collect in one place information about observational targets (e.g., rocks, soils) identified and named during Mars surface exploration. The MTE links targets to publications that provide information (e.g., composition, provenance, interpretation) about each target. Mission science team members, planetary science researchers, educators, students, and the general public can find relevant information about targets of interest or compare targets found by different missions. The MTE database will be released through the PDS and searchable through the Analyst's Notebook.




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  Scientific Understanding from Data Science (SUDS) Council

  We aim to measurably improve JPL’s scientific understanding and productivity within Astrophysics, Earth and Planetary sciences by infusing data science techniques, such as uncertainty quantification, model-based inference, machine learning, and artificial intelligence, that can directly support science and its applications. The council shall develop a strategy that advances JPL leadership, in collaboration with Campus, in scientific understanding based on state-of-the-art data science methodologies. This council will first evaluate the current state of practice at JPL for scientific understanding from data science methods.





Past Projects

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  Adaptive Data Processing for Next Generation Radio Arrays

  We utilize machine learning methods to enable large-scale radio astronomy data analyses in real-time. Specifically, we focus on the detection and characterization of time-varying sources, one of the major scientific goals of radio arrays.




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  Agricultural Research Via Interactive, Scalable Technology

  We developed and demonstrated a machine learning analysis toolkit that uses Support Vector Machines, clustering, and regression models to identify the connections between weather and agriculture.




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  Detecting Transient Surface Features with Dynamic Landmarking

  We have developed methods to dynamically and autonomously detect transient surface features, such as dust devil tracks or dark slope streaks on Mars, from images. Most prior work on this subject has relied on manual examination of image pairs. Exciting discoveries of new surface features such as gullies and impact craters have been made, usually serendipitously. How many more such features remain undiscovered in the massive volume of images being collected and returned?
  
  Automated methods can help reduce the manual effort needed to find and catalog new and interesting features. Previous techniques for automated analysis have focused on changes at the pixel level. They require an initial, sometimes slow, full registration between a candidate pair of images. Once the images are registered, subtracting one from the other yields changes. These are usually thresholded or subjected to further analysis to help filter out noise and other uninteresting "changes".




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  IMBUE: Interactive Machine Learning for Big Data Understanding and Explanation

  We seek to develop innovative software solutions to big data problems, including the overwhelming data volumes in streaming applications, massive data archives, and in-situ operations with limited communication bandwidth.




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  MISR Automated Cloud Classification

  By comparing images of the same area of the earth from different angles, scientists are able to identify thin clouds and determine approximate cloud heights with unprecedented accuracy.