Publications

  1. Faghmous, J.H., M. Le, M. Uluyol, S. Chatterjee, and V. Kumar. Parameter-Free Spatio-Temporal Data Mining to Catalogue Global Ocean Dynamics. IEEE International Conference on Data Mining (ICDM) 2013.
  2. Faghmous, J. H., and Kumar, V. (2013). Spatio-Temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities. In W. Chu (Ed.), Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenges, and Opportunities.
  3. Faghmous, J. H., Uluyol, M., Styles, L., Le, M., Mithal, V., Boriah, S., and Kumar, V. (2011). Multiple Hypothesis Object Tracking For Unsupervised Self-Learning : An Ocean Eddy Tracking Application . In AAAI-13: Twenty-Seventh Conference on Artificial Intelligence (pp. 1277–1283).
  4. Faghmous, J. H., Styles, L., Mithal, V., Boriah, S., Liess, S., F. Vikebø, M. dos Santos Mesquita, and Kumar, V. (2012). EddyScan: A physically consistent ocean eddy monitoring application. In 2012 Conference on Intelligent Data Understanding (pp. 96–103).
  5. Faghmous, J. H., Styles, L., Gibson, N., and Kumar V. (2012). Spatio-temporal data mining methods for ocean eddy monitoring. The Second International Workshop on Climate Informatics, Boulder, CO, USA, September 20–21, 2012.
  6. Faghmous, J. H., Chamber, Y., Boriah, S., Liess, S., F. Vikebø, M. dos Santos Mesquita, and Kumar, V. (2012). A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring. In AAAI-12: Twenty-Sixth Conference on Artificial Intelligence.