The Science Data Understanding Group carries out research and technology development in computational techniques to extract knowledge from science data, and distribute it to science users.

We believe that new computational capabilities, from detectors to flight and ground computers and through to the Internet, are revolutionizing the way science phenomena are captured as digital data, the way knowledge is extracted from these data, and the way data and knowledge are exchanged by the scientific community.

What we do: Model the behavior and statistical variability of physical systems like oceans and atmospheres, recognize patterns, infer parameters, and quantify uncertainty in massive data sets taken from such systems, and distribute the resulting data in a grid or virtual observatory context.

Our work covers these general topic areas:

Physical Modeling and Inversion

  • Inversion of cosmic microwave background to recover spherical harmonic power, and structural parameters of cosmological models (Wilkinson Microwave Anisotropy Probe and Planck)
  • Developing new methods for assessing agreement between climate model simulations and observations in a statistical context
  • Radiative transfer modeling in support of CO2 measurements based on laser absorption
  • Data assimilation and Bayesian risk assessment for highly nonlinear weather systems
  • Spacecraft trajectory generation with noise and uncertainty models

Pattern Recognition, Data Fusion, and Massive Dataset Analysis

  • Developed a leading system for recognizing, grouping, and tracking solar active regions used for SOHO and distributed as a data product for SDO
  • Developed multi-observation compressed summaries for MISR and AIRS, which have been distributed as a Level 3 data product
  • Spatial and spatio-temporal data fusion for atmospheric fields in a Bayesian context
  • Proprioceptive and appearance-based terrain classification for autonomous rover navigation (DARPA, ARO)
  • Classification of astronomical events and follow-up observation recommendation for astronomical transients
  • Anomaly detection, search, and behavior classification in engineering time series from ISS

Web Services, Virtual Observatories, Mapping, GIS

  • GIS for Mars landing site selection and Mars surface operations
  • GIS for California water flow analysis
  • Oceanographic data portals for salinity, sea surface temperature, hurricane study, and ocean field campaigns.
  • Generation of whole-Earth Landsat mosaics and deployment as web service (OnEarth), with extensions to Moon and Mars
  • Science-grade sky mosaicking as a grid service as a component of NVO