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  • Subsurface Energy and Environmental Systems (SEES) Lab

     

  • General Research Areas

    ResearchAreas

  • Subsurface Energy, Water, and Environmental ApplicationsApplication

  • Recent SEES Group Meeting

  • Complex High-Dimensional Geological Systems Effective description

  • Sparse Representations for Dimensionality Reduction
    Effective description

  • Inverse Modeling for Dynamic Data IntegrationKalman Filter

  • Data Science and Machine Learning for Subsurface Flow 

  • Model Predictive Control and Optimization for Field Operation and Management
    Optimization

  • Recent SEES Alumni

The Subsurface Energy and Environmental Systems (SEES) lab at USC focuses on developing customized AI and Machine Learning solutions for advanced inference and prediction in subsurface energy and industrial applications. Our interdisciplinary research integrates Machine Learning, Applied Mathematics, Estimation and Control, and Computational Geosciences, with the goal of creating cutting-edge solutions for optimizing the recovery of subsurface energy resources while minimizing environmental impacts. The lab is supported by diverse funding sources, including government agencies, private industry, and non-profit foundations. Within the Viterbi School of Engineering, we are affiliated with the Mork Family Department of Chemical Engineering and Materials Science, Ming Hsieh Departmental of Electrical Engineering, and Sonny Astani Departmental of Civil and Environmental Engineering.

University of Southern California