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 Science & Adaptation

1. Wang, D., et al. (2015), Intensification and spatial homogenization of coastal upwelling under climate change.Nature

2. Mishra, Vimal, et al.,(2015), Changes in observed climate extremes in global urban areas. ,Environmental Research Letters

3. Ghosh, Subimal, et al. (2012), Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes.,Nature Climate Change

4.Ganguly, Auroop R., et al. (2009), Higher trends but larger uncertainty and geographic variability in 21st century temperature and heat waves. Proceedings of the National Academy of Sciences

5.Khan, Shiraj, et al.(2007), Spatio‐temporal variability of daily and weekly precipitation extremes in South America., Water resources research

6.Kao, Shih‐Chieh, and Auroop R. Ganguly. (2011), Intensity, duration, and frequency of precipitation extremes under 21st‐century warming scenarios., Journal of Geophysical Research


Resilient Engineering

1.Bhatia, Udit, et al. (2015), Network science based quantification of resilience demonstrated on the Indian Railways Network., PloS one

2.Ganguly, Auroop R., et al. (2015), Climate Adaptation Informatics: Water Stress on Power Production., Computing in Science & Engineering

3. Omitaomu, Olufemi, et al. (2011), Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data.,Sensors Journal, IEEE

4. Omitaomu, Olufemi, et al. (2009), Anomaly detection in radiation sensor data with application to transportation security., Intelligent Transportation Systems, IEEE Transactions


Data Sciences

1.Das, Debasish, Auroop R. Ganguly, and Zoran Obradovic.A Bayesian Sparse Generalized Linear Model With an Application to Multiscale Covariate Discovery for Observed Rainfall Extremes Over the United States. (2015)., Transactions on Geoscience and Remote Sensing, IEEE Transactions

2. Salvi, Kaustubh, Subimal Ghosh, and Auroop R. Ganguly. (2015), Credibility of statistical downscaling under nonstationary climate., Climate Dynamics

3. Das, D., et al. (2014), Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling., Nonlinear Processes in Geophysics

4.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

5. Chatterjee, S. et al. (2012), Sparse Group Lasso: Consistency and Climate Applications., SIAM International Conference on Data Mining (SDM 2012), Best Student Paper Award at SDM 2012

6. Khan, Shiraj, et al. (2007) ,Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data., Physical Review