Faghmous, J.H., M. Le, M. Uluyol, and V. Kumar. Parameter-Free Spatio-Temporal Data Mining to Catalogue Global Ocean Dynamics. Thirteenth IEEE International Conference on Data Mining (ICDM) 2013.
Faghmous, J.H., M. Uluyol, M. Le, L. Styles, V. Mithal, S. Boriah, and V. Kumar. Multiple Hypothesis Object Tracking for Unsupervised Self-Learning: An Ocean Eddy Tracking Application. Twenty-Seventh Conference on Artificial Intelligence (AAAI) 2013.
Faghmous, J.H., L. Styles, F. Vikebø, S. Boriah, S. Liess, M. d.S. Mesquita, and V. Kumar. EddyScan: A Physically Consistent Eddy Monitoring Application. Conference on Intelligent Data Understanding (CIDU) 2012. Best Student Paper Award
Faghmous, J.H., Y. Chamber, F. Vikebø, S. Boriah, S. Liess, M. d.S. Mesquita, and V. Kumar. A Novel Spatio- Temporal Method for Ocean Eddy Monitoring. Twenty-Sixth Conference on Artificial Intelligence (AAAI) 2012.
Ganguly, A.R., Kodra, E. A., Banerjee, A., Boriah, S., Chatterjee S., Chatterjee, S., Choudhary, A., Das, D., Faghmous, J.H., et al. Toward enhanced understanding and prediction of climate extremes using physics-guided data mining techniques. Nonlinear Processes in Geophysics 2014.
Faghmous, J.H., M. Le, S. Liess, V. Kumar, and K. Emanuel. ENSO’s Spatial Warming Patterns and their Impact on North Atlantic Tropical Cyclone Activity. In preparation.
Faghmous, J.H. and V. Kumar. 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. Springer, 2013.
Karpante A., Faghmous J.H., Kawale J., et al. Earth Science Applications of Sensor Data. In C. Aggarwal, Ed., Managing and Mining Sensor Data. Springer, 2012.