Auroop R Ganguly is the Principal Investigator of the Sustainability and Data Sciences Laboratory (SDS Lab) and an Associate Professor at the department of Civil and Environmental Engineering at Northeastern University.

Selected Publications: Climate Extremes

1. Kodra, E., Ganguly, A.R. (2014):Asymmetry of projected increases in extreme temperature distributions. Scientific Reports 4, Article no: 5884, doi:10.1038/srep05884.

2. Ghosh, S., Das, D., Kao, S.-C., and A.R. Ganguly, (2012):Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Climate Change 2(2), 86-91, doi:10.1038/nclimate1327. Highlighted by the National Science Foundation (Selected) News: American Meteorological Society, Science Daily, Earth Times, Deccan Herald (India)

3.Kao, S.C., and A.R. Ganguly (2011): Intensity, duration, and frequency of precipitation extremes under 21st-century warming scenarios. Journal of Geophysical Research – Atmospheres, 116, D16119, 14 PP., DOI: 10.1029/2010JD015529.

4. Kodra, E., Steinhaeuser, K.S., and A.R. Ganguly (2011): Persisting cold spells in the 21st-century warming environment. Geophysical Research Letters, 38, L08705, 5 pp. Research Highlights in Nature and Nature Climate Change

5.Ganguly, A.R., Steinhaeuser, K., Erickson, D.J., Branstetter, M. Parish, Singh, N., Drake, J.B., and L. Buja (2009): Higher Trends but Larger Uncertainty and Geographic Variability in 21st Century Temperature and Heat Waves. Proceedings of the National Academy of Sciences of the United States of America , 106(37), 15555-15559.

Selected Publications: Hydrological Systems

1. Parish, E., Kodra, E., Steinhaeuser, K., and A.R. Ganguly (2012). Estimating future global per capita water availability based on changes in climate and population, Computers & Geosciences,42: 79-86. Citation by Science Direct: Top 25 Hottest Articles in Computers & Geosciences from April-June 2012

2.Khan, S., Kuhn, G., Ganguly, A.R., Erickson, D.J., and G. Ostrouchov (2007): Spatio-temporal variability of daily and weekly precipitation extremes in South America. Water Resources Research, 43, W11424.

3. Kuhn, G., Khan, S., Ganguly, A.R., M. Branstetter (2007): Geospatial-temporal dependence among weekly precipitation extremes with applications to observations and climate model simulations in South America. Advances in Water Resources, 30(12): 2401-23. Citation by journal: Top 25 “hottest” papers in terms of number of downloads

4.Khan, S.,Ganguly, A.R.,Bandyopadhyay,S.,Saigal, S., Erickson, D.J., Protopopescu, V., and G. Ostrouchov (2006): Non-linear Statistics reveals stronger ties between ENSO and the tropical hydrological cycle. Geophysical Research Letter, 33, L24402,6 PP., DOI: 10.1029/2006GL027941.

5. Ganguly, A.R., and R.L. Bras (2003): Distributed quantitative precipitation forecasting using information from radar and numerical weather prediction models. Journal of Hydrometeorology, 4 (6): 1168-180.

Selected Publications: Data Sciences

1.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

2. 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

3.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.

4.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.

5.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.

Selected Publications: Complex Systems

1. 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.