Auroop R. Ganguly

About

Auroop R Ganguly, with nearly 26 years of professional experience spanning private industry, government, and academia, is a College of Engineering Distinguished Professor at Northeastern University (NU) in Boston, MA. He directs the Sustainability and Data Sciences Laboratory (SDS Lab) and the AI for Climate and Sustainability (AI4CaS) focus area within the Institute for Experiential AI (EAI) and previously co-directed the Global Experience Institute (GRI). He holds a joint appointment as a Chief Scientist at the Pacific Northwest National Laboratory (PNNL) in their Advanced Computing, Mathematics, and Data Division.

Ganguly was previously employed as a Senior Scientist at the US DOE’s Oak Ridge National Laboratory (ORNL) in their Computational Sciences and Engineering Division and their Climate Change Science Initiative. He also worked with Oracle Corporation, first as a Time Series Software Developer and then as the Product Manager of their Demand Planning product from launch to market acceptance, and subsequently as the Senior Product Manager for Analytics and Strategy at a best-of-breed company called Demantra Inc., later acquired by Oracle.

Ganguly’s convergent research themes include process knowledge integrated data-driven sciences such as machine learning, nonlinear dynamics, network science and optimization, and extreme value theory. He also focuses on the predictive understanding of climate and weather systems and resilience of lifeline infrastructure or ecological networks, and water resources systems, along with their interactions with human and policy dimensions for resilience to climate, security, and compound extremes. His research has been published in high-impact journals such as Nature, Nature Climate Change, and PNAS, as well as disciplinary journals in science, engineering, and computational methods. He has received best-paper awards at highly-selective computer science conferences such as ACM KDD and SIAM Data Mining.

He has authored a textbook on Critical Infrastructure Resilience and edited books on Knowledge Discovery from Sensor Data. Ganguly has been widely quoted in the mainstream media, including the New York Times, Newsweek, Guardian, and the Independent, both in the US and around the world. He has also been featured in scientific venues such as National Geographic, Nature Research Briefings, and NSF News. He has been invited as a keynote and plenary speaker at National Academy workshops and prestigious conferences, elected as lead author in urban and national climate resilience assessments, and cited in assessment reports and invited for panels of the United Nations in areas such as climate, environment, and disaster risk reduction.

He has been a co-founder and advisor to successful and innovative startups in urban climate risk analytics and AI-based precision weather forecasts and is the inventor of two US patents, one in climate risks and the other in network resilience. Ganguly has been a PI, Co-PI, or senior person for nearly $65M of research funding received over the years from agencies such as NSF, DOE, DOD, DHS, and NASA, as well as private donors and the private-public sector, and the governments of India and Indonesia.

He obtained a PhD from the civil and environmental engineering department at the Massachusetts Institute of Technology in Cambridge, MA, in hydrology, an MS from the University of Toledo in Ohio in civil and environmental engineering, and a B.Tech. (Hons.) from the Indian Institute of Technology at Kharagpur in civil engineering. His former students, postdocs, and mentees have been widely successful as startup entrepreneurs, industry leaders, private and government researchers, and university professors, both nationally and across the world. Ganguly is a Fellow of the American Society of Civil Engineers (ASCE) and a Distinguished Member of the Association for Computing Machinery (ACM).

Thirty Six Selected Publications (led by Ganguly and/or his students/postdocs/mentees)

• Yadav, N.*, Sorek-Hamer, M., Von Pohle, M., Asanjan, A.A., Sahasrabhojanee, A., Suel, E., Arku, R.E., Lingenfelter, V.*, Brauer, M., Ezzati, M., Oza, N., and Ganguly, A.R., 2024. Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions. Environmental Pollution, 342, 122914.
• Bhatia, U.*, Dubey, S., Gouhier, T.C. and Ganguly, A.R., 2023. Network-based restoration strategies maximize ecosystem recovery. Communications Biology, 6, 1256.
• Liu, Y.*, Duffy, K.*, Dy, J.G. and Ganguly, A.R., 2023. Explainable deep learning for insights in El Niño and river flows. Nature Communications, 14, 339.
• Duffy, K.*, Vandal, T.J.*, Wang, W., Nemani, R.R. and Ganguly, A.R., 2023. A framework for deep learning emulation of numerical models with a case study in satellite remote sensing. IEEE Transactions on Neural Networks and Learning Systems, 34(7), 3345-3356.
• Ashfaq, M., Johnson, N., Kucharski, F., Diffenbaugh, N.S., Abid, M.A., Horan, M.F., Singh, D., Mahajan, S., Ghosh, S., Ganguly, A.R. and Evans, K.J., 2023. The influence of natural variability on extreme monsoons in Pakistan. npj Climate and Atmospheric Science, 6, 148.
• Sharma, B.*, Kumar, J., Ganguly, A.R. and Hoffman, F.M., 2023. Carbon cycle extremes accelerate weakening of the land carbon sink in the late 21st century. Biogeosciences, 20(10), 1829–1841.
• Duffy, K.*, Gouhier, T.C. and Ganguly, A.R., 2022. Climate-mediated shifts in temperature fluctuations promote extinction risk. Nature Climate Change, 12, 1037-1044.
• Sharma, B.*, Kumar, J., Collier, N., Ganguly, A.R. and Hoffman, F.M., 2022. Quantifying carbon cycle extremes and attributing their causes under climate and land use and land cover change from 1850 to 2300. Journal of Geophysical Research: Biogeosciences, 127(6), e2021JG006738.
• Watson, J.R.*, Chatterjee, S. and Ganguly, A., 2022. Resilience of Urban Rail Transit Networks under Compound Natural and Opportunistic Failures. In 2022 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-8), November.
• Konduri, V.*, Breen, A., Hargrove, W., Hoffman, F., Salmon, V., Iversen, C., Ganguly, A. and Kumar, J., 2021. Hyperspectral remote sensing-based plant community map for region around NGEE-Arctic intensive research watersheds at Seward Peninsula, Alaska, 2017-2019 (No. NGA266). Next Generation Ecosystems Experiment-Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); NGEE Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States).
• Ganguly, A.R. and colleagues, 2021. Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE) (No. AI4ESP1047). AI4ESP: White Paper, US DOE BER Call. AI4ESP and US DOE.
• Kodra, E.*, Bhatia, U.*, Chatterjee, S., Chen, S.* and Ganguly, A.R., 2020. Physics-guided probabilistic modeling of extreme precipitation under climate change. Scientific Reports, 10, 10299.
• Yadav, N.*, Chatterjee, S. and Ganguly, A.R., 2020. Resilience of urban transport network-of-networks under intense flood hazards exacerbated by targeted attacks. Scientific Reports, 10, 10350.
• Konduri, V.S.*, Kumar, J., Hargrove, W.W., Hoffman, F.M. and Ganguly, A.R., 2020. Mapping crops within the growing season across the United States. Remote Sensing of Environment, 251, 112048.
• Bhatia, U.*, Sela, L. and Ganguly, A.R., 2020. Hybrid method of recovery: combining topology and optimization for transportation systems. Journal of Infrastructure Systems, 26(3), 04020024.
• Bhatia, U.* and Ganguly, A.R., 2019. Precipitation extremes and depth-duration-frequency under internal climate variability. Scientific Reports, 9, 9112.
• Duffy, K.*, Vandal, T.*, Li, S., Ganguly, S., Nemani, R. and Ganguly, A.R., 2019. DeepEmSat: Deep Emulation for Satellite Data Mining. Frontiers in Big Data, 2, 42.
• Kumar, D.* and Ganguly, A.R., 2018. Intercomparison of model response and internal variability across climate model ensembles. Climate Dynamics, 51(1), 207-219.
• Clark, K.L.*, Bhatia, U.*, Kodra, E.A.* and Ganguly, A.R., 2018. Resilience of the US national airspace system airport network. IEEE Transactions on Intelligent Transportation Systems, 19(12), 3785-3794.
• Vandal, T.*, Kodra, E.*, Dy, J., Ganguly, S., Nemani, R. and Ganguly, A.R., 2018, July. Quantifying uncertainty in discrete-continuous and skewed data with Bayesian deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2377-2386).
• Vandal, T.*, Kodra, E.*, Ganguly, S., Michaelis, A., Nemani, R. and Ganguly, A.R., 2017, August. DeepSD: Generating high resolution climate change projections through single image super-resolution. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (1663-1672).
• Ganguli, P.* and Ganguly, A.R., 2016. Space-time trends in US meteorological droughts. Journal of Hydrology: Regional Studies, 8, 235-259.
• Ganguli, P.* and Ganguly, A.R., 2016. Robustness of meteorological droughts in dynamically downscaled climate simulations. JAWRA Journal of the American Water Resources Association, 52(1), 138-167.
• Wang, D.*, Gouhier, T.C., Menge, B.A. and Ganguly, A.R., 2015. Intensification and spatial homogenization of coastal upwelling under climate change. Nature, 518(7539), 390-394.
• Bhatia, U.*, Kumar, D.*, Kodra, E.* and Ganguly, A.R., 2015. Network science based quantification of resilience demonstrated on the Indian Railways Network. PLOS ONE, 10(11), e0141890.
• Kodra, E.* and Ganguly, A.R., 2014. Asymmetry of projected increases in extreme temperature distributions. Scientific Reports, 4, 5884.
• Kumar, D.*, Kodra, E.* and Ganguly, A.R., 2014. Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Climate Dynamics, 43(9-10), 2491-2518.
• Steinhaeuser, K.*, Ganguly, A.R. and Chawla, N.V., 2012. Multivariate and multiscale dependence in the global climate system revealed through complex networks. Climate Dynamics, 39(3-4), 889-895.
• Kao, S.C.* and Ganguly, A.R., 2011. Intensity, duration, and frequency of precipitation extremes under 21st‐century warming scenarios. Journal of Geophysical Research: Atmospheres, 116(D16).
• Kodra, E.*, Steinhaeuser, K.* and Ganguly, A.R., 2011. Persisting cold extremes under 21st‐century warming scenarios. Geophysical Research Letters, 38(8).
• Steinhaeuser, K.*, Chawla, N.V. and Ganguly, A.R., 2011. Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4(5), 497-511.
• Ganguly, A.R., Steinhaeuser, K.*, Erickson, D.J., Branstetter, M., Parish, E.S., Singh, N., Drake, J.B. and Buja, L., 2009. Higher trends but larger uncertainty and geographic variability in 21st century temperature and heat waves. Proceedings of the National Academy of Sciences, 106(37), 15555-15559.
• Khan, S.*, Bandyopadhyay, S., Ganguly, A.R., Saigal, S., Erickson III, D.J., Protopopescu, V. and Ostrouchov, G., 2007. Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data. Physical Review E, 76(2), 026209.
• Kuhn, G.*, Khan, S.*, Ganguly, A.R. and Branstetter, M.L., 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), 2401-2423.
• Khan, S.*, Kuhn, G.*, Ganguly, A.R., Erickson III, D.J. and Ostrouchov, G., 2007. Spatio‐temporal variability of daily and weekly precipitation extremes in South America. Water Resources Research, 43(11).

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