About

Auroop R. Ganguly is a Distinguished Professor at Northeastern University in Boston, MA, where he has been employed in the College of Engineering since the Fall of 2011. He holds courtesy appointments with the Khoury College of Computer Science and the School of Public Policy and Urban Affairs. With nearly 28 years of full-time professional research and leadership experience across the private technology sector, government-owned national laboratories, and academia, Ganguly's foundational research focuses on adaptation science and technology to global change. His work has been published and highlighted in interdisciplinary journals such as Nature, Science, and PNAS, and spans disciplines including hydrology, meteorology, climate, transportation, ecology, biology, urban systems, security, physics, statistics, risk analysis, law, and policy. His research integrates physics, engineering, and policy with data sciences like machine learning, nonlinear dynamics, and extreme value statistics to address weather extremes, infrastructure resilience, and sustainability challenges. Ganguly's contributions extend to community impacts in Boston, nationally, and globally, with entrepreneurial activities resulting in US patents and five startup companies, one of which was acquired by a Fortune 500 company. He has delivered keynotes at US National Academy workshops, served on panels and workshops of the United Nations, and been involved in approximately $70 million in research funding. Currently, he directs the Sustainability and Data Sciences Laboratory and the AI for Climate and Sustainability focus area within the Institute for Experiential AI at Northeastern University. His research aims to harness AI and process knowledge to develop innovative solutions for climate risk, resilience, and energy sustainability, working with public, private, and government partners worldwide.

Research topics

• Computer Science
• Ecology
• Environmental science
• Mathematics
• Geography
• Artificial Intelligence
• Political Science
• Econometrics
• Geology
• Biology
• Forestry
• Atmospheric sciences
• Environmental resource management
• Climatology
• Remote sensing
• Statistics
• Agronomy

Selected publications

• Climate-mediated shifts in temperature fluctuations promote extinction risk

  Nature Climate Change · 2022 · 90 citations

  Senior authorCorresponding
  • Computer Science
  • Environmental science
  • Climatology

  Abstract Climate-mediated changes in thermal stress can destabilize animal populations and promote extinction risk. However, risk assessments often focus on changes in mean temperatures and thus ignore the role of temporal variability or structure. Using Earth System Model projections, we show that significant regional differences in the statistical distribution of temperature will emerge over time and give rise to shifts in the mean, variability and persistence of thermal stress. Integrating these trends into mathematical models that simulate the dynamical and cumulative effects of thermal stress on the performance of 38 globally distributed ectotherm species revealed complex regional changes in population stability over the twenty-first century, with temperate species facing higher risk. Yet despite their idiosyncratic effects on stability, projected temperatures universally increased extinction risk. Overall, these results show that the effects of climate change may be more extensive than previously predicted on the basis of the statistical relationship between biological performance and average temperature.

  PublisherOA PDFDOI

• Data Science for Weather Impacts on Crop Yield

  Frontiers in Sustainable Food Systems · 2020 · 67 citations

  Senior authorCorresponding
  • Computer Science
  • Political Science
  • Econometrics

  Private businesses in sectors such as food, energy and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.

  DOI

• Mapping crops within the growing season across the United States

  Remote Sensing of Environment · 2020 · 116 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Remote sensing

  Timely and accurate knowledge about the geospatial distribution of crops at regional to continental scales is crucial for forecasting crop production and estimating crop water use. The United States (US) is one of the leading food-producing countries, but lacks a nationwide high resolution crop-specific land cover map available publicly during the current growing season. The goal of this study was to map crops across the Continental US (CONUS) before the harvest, and to estimate the earliest date of classification by which crops can be mapped with sufficient accuracy (90% of full-season accuracy). The study employed a scalable cluster-then-label model that was trained on multiple years of MODIS NDVI using ground truth data in the form of US Department of Agriculture (USDA) Cropland Data Layer (CDL) products. The first step in the crop classification was to perform Multivariate Spatio-Temporal Clustering (MSTC) of annual MODIS-derived NDVI trajectories to create phenologically similar regions, or phenoregions. The second step was to assign crop labels to phenoregions based on spatial concordance between phenoregions and crop classes from CDL using Mapcurves. Assigning crop labels to phenoregions was performed within ecoregions to reduce classification errors due to spatial variability in phenology caused by variations in climate, agricultural practices, and growing conditions. The crop classifier was trained and validated on the years 2008–2014, then tested independently on 2015–2018. Ecoregion-level crop classification performed better than state-level and CONUS-level classification. Pixel-wise accuracy of classification for eight major crops by area was around 70% across the major corn-, soybeans- and winter wheat-producing areas, whereas regions characterized by high crop diversity had slightly lower accuracy. Classification accuracy for dominant crops like corn, soybeans, winter wheat, fallow/idle cropland and other hay/non alfalfa improved with time as they grew, reaching 90% of year-end accuracy by the end of August over each of the four unseen years in the test period. For corn and soybeans, the earliest dates of classification were found to be much earlier in the central regions of the Corn Belt (parts of Iowa, Illinois and Indiana) than in peripheral areas. The ability to map growing crops may permit near real-time monitoring of the health status and vigor of agricultural crops nationally.

  DOI

Recent grants

• BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data

  NSF · $357k · 2014–2019

Frequent coauthors

• Udit Bhatia

  Indian Institute of Technology Gandhinagar

  58 shared

• Evan Kodra

  Northeastern University

  45 shared

• Karsten Steinhaeuser

  41 shared

• Thomas Vandal

  38 shared

• Devashish Kumar

  35 shared

• Kate Duffy

  32 shared

• Ramakrishna Nemani

  30 shared

• Nishant Yadav

  24 shared

Labs

• Sustainability and Data Sciences Laboratory (SDS Lab)PI

Awards & honors

• Fellow, American Society of Civil Engineers
• Distinguished Member, Association for Computing Machinery
• Daffodil Lecture, Commonwealth Honors College, UMass Amherst
• Senior Member, Institute of Electrical and Electronics Engin…
• College of Engineering Distinguished Professor, Northeastern…

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