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Our research focuses on efficient development of subsurface energy, water, and environmental resources. To this end, we develop novel algorithmic solutions to challenging problems concerning the prediction of flow and transport phenomena in complex subterranean environments. A grand challenge in this area is related to accurate representation and simulation of physical and chemical processes that take place in heterogeneous geological formations. A major contributor to this challenge is our inability to see into these formations to better understand their properties and the behavior of the underlying processes. The difficulty in observing these systems introduces significant uncertainty into subsurface modeling, characterization, and flow and transport forecasting, and necessitates proper representation and quantification of uncertainty in modeling them. At the SEES lab, we integrate mathematical methods with physical insight about subsurface properties and the related flow and transport processes to address these challenges.

The research activities of our lab are concentrated on developing computational and mathematical techniques for description, characterization, prediction, and optimization of subsurface flow and transport systems by taking into account the existing geologic uncertainties, data deficiencies, and dynamics of fluid flow in porous material.