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Chengkai Fan is an Assistant Professor in the Department of Mining, Metallurgy and Materials Engineering at Laval University since September 2024. With over a decade of interdisciplinary expertise in mining and geotechnical engineering, his research primarily revolves around developing interpretable artificial intelligence models applied to mining transportation. He specializes in predicting the productivity and optimizing truck mining systems using machine learning techniques, particularly in extreme or uncertain climatic conditions. He also focuses on modeling data from sensors, such as fiber optic sensors, to support intelligent monitoring and decision-making. His research includes experimental and numerical studies on fluid migration and geomechanical responses related to geological CO2 storage. Dr. Fan leads a research group dedicated to advancing data-driven modeling, optimization, and intelligent decision-making for sustainable and safe mining systems. His lab leverages artificial intelligence and detection technologies as essential tools to improve mining transportation efficiency, enhance mine safety, and support carbon management.
Laval University • Canada
Teaching and conducting research in mining, metallurgy, and materials engineering.
University of Alberta • Canada
Delivered lectures and supervised students in the mining engineering program.
University of Alberta • Canada
Conducting advanced research in mining engineering.
Monash University • Australia
Collaborated on projects related to geotechnical engineering.
Department of Management / MBA programs often require higher GPAs and specific English proficiency.