1. Ganguly, A.R. Kodra, E.A., Banerjee, A., Boriah, S., Chatterjee, S., Chatterjee, S., Choudhary, A., Das, D., Faghmous, J., Ganguli, P., Ghosh, S., Hayhoe, K., Hays, C., Hendrix, W., Fu, Q., Kawale, J., Kumar, D., Kumar, V., Liess, S., Mawalagedara, R., Mithal, V., Oglesby, R., Salvi, K., Snyder, P.K., Steinhaeuser, K., Wang, D., and D. Wuebbles (2014):Toward enhanced understanding and prediction of climate extremes using physics-guided data mining techniques. Nonlinear Processes in Geophysics.(Accepted: In Print).
  2. Steinhaeuser, K., Chawla, N.V., and A.R. Ganguly (2011): Complex networks as a unified framework fo descriptive analysis and predictive modeling in climate science. Statistical Analysis and Data Mining, 4(5), 497-511.
  3. Omitaomu, O.A., Protopopescu, V.A., and A.R. Ganguly (2011): Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data. IEEE Sensors Journal, DOI: 10.1109/JSEN.2011.2142302.
  4. Kodra, E., Chatterjee, S., and A.R. Ganguly (2011): Exploring Granger causality between global average observed time series of carbon dioxide and temperature. Theoretical and Applied Climatology, 104(3-4): 325-335.
  5. Steinhaeuser, K., Chawla, N.V., and A.R. Ganguly (2010): An exploration of climate data using complex networks. ACM SIGKDD Explorations, 12(1): 25-32.
  6. Huang, C., Hsing, T., Cressie, N., Ganguly, A.R., Protopopescu, V.A., and N.S. Rao (2010): Bayesian sources detection and parameters estimation of plume model based on sensor network measurements. Applied Stochastic Models in Business and Industry, 26(4): 331-348.
  7. Omitaomu, O.A., Ganguly, A.R., Patton, B.W., and V.A. Protopopescu (2009): Anomaly detection in radiation sensor data with application to transportation security. IEEE Transactions on Intelligent Transportation Systems, 10(2): 324-334.
  8. Agovic, A., Banerjee, A., Ganguly, A.R. and V. Protopopescu (2009): Anomaly detection using manifold embedding and its applications in transportation corridors. Intelligent Data Analysis, 13(3): 435-455.
  9. Khan, S., S. Bandyopadhyay, A.R. Ganguly, S. Saigal, D. J. Erickson, III, V. Protopopescu, and G. Ostrouchov (2007): Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data. Physical Review E, 026209.
  10. Reyes-Aldasoro, C.C., Ganguly, A.R., Lemus, G., and A Gupta (1999): A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimization applications. Journal of the Operational Research Society, 50(1), 85-94.
   Selected Peer-Reviewed Conferences:
  1. Vatsavai, Ranga Raju, Auroop Ganguly, Varun Chandola, Anthony Stefanidis, Scott Klasky, and Shashi Shekhar (2012): Spatiotemporal data mining in the era of Big Spatial Data: algorithms and applications. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, pp. 1-10. ACM, 2012.
  2. Chatterjee, S., Steinhaeuser, K., Banerjee, A., Chatterjee, S., and A.R. Ganguly (2012): Sparse Group Lasso: Consistency and Climate Applications. 2012 SIAM International Conference on Data Mining (SDM 2012), Anaheim, CA, April 26-28, 2012. Oral Presentation and Full Paper (Acceptance Rate: 15%). Best Student Paper Award at SDM 2012
  3. Kawale, J., Liess, S., Kumar, A., Steinbach, M., Ganguly, A.R., Samatova, N.F., Semazzi, F., Snyder, P., and V. Kumar (2011): Data-guided discovery of climate dipoles in observations and models. 2011 NASA Conference on Intelligent Data Understanding (NASA CIDU 2011), Mountain View, CA, October 20-21, 2011. Best Student Paper Award at NASA CIDU 2012
  4. Faghmous, J.H., Liess, S., Ganguly, A., Steinbach, M., Semazzi, F., and V. Kumar (2011): Data mining technique suggests a dynamic relationship between Atlantic sea surface temperatures and hurricanes. 2011 NASA Conference on Intelligent Data Understanding (NASA CIDU 2011), Mountain View, CA, October 20-21, 2011.
  5. Hoffman, F.M., Larson, J.W., Mills, R.T., Brooks, B.J., Ganguly, A.R., Hargrove, W.W., Huang, J., Kumar, J., and R.R. Vatsavai (2011): Data Mining in Earth System Science (DMESS 2011). Proceedings of the International Conference on Computational Science, ICCS 2011, Nanyang Technological University, Singapore, 1-3 June 2011.
  6. Steinhaeuser, K., Chawla, N., and A.R. Ganguly (2011): Comparing predictive power in climate data: clustering matters. 12th International Symposium on Spatial and Temporal Databases. Twin Cities, MN, USA, August 24-26, 2011.
  7. Pelan, A., Steinhaeuser, K., Chawla, N.V., de Alwis Pitts, D.A., and A.R. Ganguly (2011): Empirical Comparison of Correlation Measures and Pruning Levels in Complex Networks Representing the Global Climate System. IEEE Symposium Series on Computational Intelligence and Data Mining (CIDM), Paris, France.
  8. Steinhaeuser, K., Chawla, N.V., and A.R. Ganguly (2009): An exploration of climate data using complex networks. 3rd International Workshop on Knowledge Discovery from Sensor Data, 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Best Student Paper Award.
  9. Kao, S.-C., Ganguly, A.R., and K. Steinhaeuser (2009): Motivating complex dependence structures in data mining: Case study with anomaly detection in climate data. 9th IEEE International Conference on Data Mining - Workshops (ICDMW'09).
  10. A.R. Ganguly, O.A. Omitaomu, and J. Yu (2009): Information-Theoretic Approaches for Evaluating Complex Adaptive Social Simulation Systems. Proceedings of the Human Behavior-Computational Intelligence Modeling Conference, Oak Ridge, TN, June 23–24.
  11. Fang, Y., Ganguly, A.R., Singh, N., Vijayaraj, V., Feierabend, N., and D.T. Potere (2006): Online change detection: Monitoring land cover from remotely sensed data. 6th IEEE International Conference on Data Mining – Workshops (ICDMW'06).
  Selected Books, Chapters, and Editorials:
  1. Auroop R. Ganguly, Joseph Whitmeyer, Olufemi Omitaomu, Peter Brecke, Mirsad Hadžikadić, Paul Gilman, Moutaz Khouja, Steven Fernandez, Christopher Eichelberger, Thom McLean, Cathy (Yu) Jiao, Erin Middleton, Ted Carmichael, Amar Saric, Min Sun (2013):Towards a Characterization and Systematic Evaluation Framework for Theories and Models of Human, Social, Behavioral, and Cultural Processes within Agent-Based Models.Mirsad Hadžikadić, Sean O’Brien, Moutaz KhoujaPractical. Managing Complexity: Considerations in the Development and Application of ABMs to Contemporary Policy Challenges, Studies in Computational Intelligence Volume 504, pp. 93-136.
  2. Chandola, V., Omitaomu, O.A., Ganguly, A.R. Gama, J., Chawla, N.V., Gaber, M.M., and A.R. Ganguly (2010): Knowledge Discovery from Sensor Data (SensorKDD). ACM SIGKDD Explorations, 12(2): 50-53.
  3. Gama, J., Ganguly, A.R., Omitaomu, O.A., Vatsavai, R.R., and M.M. Gaber (2009): Knowledge discovery from data streams. Intelligent Data Analysis, 13(3): 403-404.
  4. Ganguly, A.R., Gama, J., Omitaomu, O.A., Gaber, M.M., and R.R. Vatsavai (Editors) (2009): Knowledge Discovery from Sensor Data. CRC Press, Taylor & Francis, New York: 216 pages.
  5. Ganguly, A.R., Omitaomu, O.A., and R.M. Walker (2007): Knowledge discovery from sensor data for security applications. In Gaber, M. and J. Gama (eds.), Learning from Data Streams – Processing Techniques in Sensor Networks, Springer-Verlag, pp. 187-204.
  6. Ganguly, A.R., Gupta, A., and S. Khan (2008): Data mining and decision support for business and science. In Hsu, J. (Ed.), Data Mining and Business Intelligence: Tools, Technologies and Applications, 2618-2625.