Faculty & Senior Researchers
University of Minnesota, Twin Cities
Vipin Kumar, Lead PI, Regents Professor and William Norris Chair, Computer Science & Engineering
Research: Data Mining, High-Performance Computing, and their applications in Climate/Ecosystems and Biomedical domains
Arindam Banerjee, McKnight Land-Grant Professor; Resident Fellow, Institute on the Environment; Computer Science & Engineering
Research: Machine Learning, Data Mining, Information Theory, Convex Analysis and Optimization, and their applications in complex real world learning problems
Snigdhansu Chatterjee, Associate Professor, Statistics
Research: My research interests are in statistical analysis and inference on large, complex datasets. I work on change detection, model selection and averaging, Bayesian modeling, resampling techniques, multivariate time series and nonparametric statistics as part of this project.
Joseph F. Knight, Assistant Professor, Forest Resources
Research: Studying how changing land use affects both natural resources and humans. Geospatial science methods such as remote sensing, image processing, and geographic information systems (GIS).
Stefan Liess, Research Associate, Soil Water, and Climate
Research: Simulation of atmospheric dynamics and climate change with general circulation models, interactions of climate and vegetation, tropical climate, predictability and sensitivity of climate, stratospheric processes
Shashi Shekhar, McKnight Distinguished University Professor, Computer Science & Engineering, Distinguished University Teaching Professor
Research: Spatial Databases, Data and Knowledge Engineering, Spatial Data Mining
Peter K. Snyder, Assistant Professor, Department of Soil, Water, and Climate and the Department of Forest Resources
Research: Interactions between the atmosphere and the biosphere with models, observations, and data analysis.
Michael Steinbach, Research Associate, Computer Science & Engineering
Research: Data mining, Bioinformatics and Statistics. Two areas of focus are data mining for Earth science data and biological / biomedical data.
North Carolina State University
Nagiza F. Samatova, Associate Professor, Computer Science
Research: Graph Theory and Algorithms, High Performance Data Analytics, Bioinformatics, Systems Biology, Data Management and Data Integration, Scientific and High Performance Computing, Natural Language Processing, Machine Learning
Fredrick H. M. Semazzi, Professor, Climate Modeling Laboratory
Research: Climate modeling, development of nonhydrostatic, semi-implicit, semi-Lagrangian, numerical integration schemes for global atmospheric prediction models, and application of climate information for decision making. Over the last two years, he has been directing the development of a science plan for a ten-year HYVIC GEWEX International Regional Hydroclimate Project (RHP) to understand the hydroclimatic variability over Lake Victoria Basin and improve its climatic predictability and climate change projections.
Auroop R. Ganguly, Associate Professor, Civil and Environmental Engineering
Research: Climate extremes and climate change uncertainties, as well as climate impacts on the water sector. Knowledge discovery from data, broadly construed to include nonlinear dynamics, applied time series and spatial statistics, spatio-temporal data mining, econometrics, and operations research.
Alok Choudhary, John G. Searle Professor and Chair Electrical Engineering and Computer Science Professor, Kellogg School of Management, Director, Center for Ultra-Scale Computing and Information Security (CUCIS)
Research: High-performance computing, data intensive computing, scalable data mining, computer architecture, high-performance I/O systems and software, their applications in many domains. The design and evaluation of architectures and software systems, high-performance servers, high-performance databases and input-output and software protection and security.
Wei-keng Liao, Research Professor
Research: High-performance computing, parallel I/O, parallel file systems, data mining, and data management for large-scale scientific applications.
Ankit Agrawal, Research Associate Professor
Research: Data Mining, High Performance Computing,Bioinformatics, Social Media Analytics, Biomedical Informatics,Climate Informatics, Materials Informatics.
North Carolina A & T State University
Abdollah Homaifar, Duke Energy Eminent Professor in Electrical and Computer Engineering, Director of the Autonomous Control and Information Technology center and Campus Director of the Center for Power Electronics Systems
Research: Machine learning, optimization, adaptive control, optimal control, signal processing, and soft computing and modeling.
Ali Karimodddini, Assistant Professor, Electrical and Computer Engineering
Research: Large scale complexsystems, robotic swarms, cyber-physical systems, cooperative and distributed control of multi-agent systems, reliable and fault tolerant hybrid control systems, aerial robotics, flight control systems, discrete event systems, distributed optimization for coordination of multi-agent systems, hybrid modeling and control and biological systems.
Fatemeh Afghah, Assistant Professor, Electrical and Computer Engineering
Research: Wireless Communication Systems, Cooperative Communication, Game Theory and Convex Optimization, Cognitive Radio Networks
Shyam Boriah, Research Associate, Computer Science & Engineering
Research: spatio-temporal data mining, time series change detection, data mining foundations, and Earth science/environmental applications
Jonathan Foley, Director of the Institute on the Environment (IonE), professor and McKnight Presidential Chair in the Department of Ecology, Evolution and Behavior.
Research: Complex global environmental systems and their interactions with human societies, global-scale ecological processes, global patterns of land use, the behavior of the planet’s climate and water cycles, and the sustainability of our biosphere.
Karsten Steinhaeuser, Research Associate, Computer Science & Engineering
Research: Data mining and machine learning, specifically the construction and analysis of networks; community detection and prediction in networks; learning from large datasets, including parallel and distributed learning algorithms; applications of the above to climate science, environmental science and sustainability, biology, ecology, medicine, medical informatics, and social networks.