Dr. Chengkai Fan has been an Assistant Professor in the Department of Mining, Metallurgical, and Materials Engineering at Université Laval since September 2024. Over the past decade, he has developed solid multidisciplinary expertise in mining and geotechnical engineering. His research focuses primarily on the development of interpretable artificial intelligence models applied to mine transport. He specializes in predicting productivity and optimizing mining truck haulage systems using machine learning techniques, particularly under extreme or uncertain climatic conditions. He also works on modeling sensor data, such as fiber optic sensors, to support intelligent monitoring and decision support. In addition, his research includes experimental and numerical studies on fluid migration and geomechanical responses related to geological CO2 storage. Dr. Fan is leading a research group dedicated to advancing data-driven modeling, optimization, and intelligent decision-making for sustainable and safe mining systems. The lab leverages artificial intelligence and sensing technologies as essential tools to improve mine transport efficiency, enhance mine safety, and support carbon management. He is a scientific editor for the Journal of Rock Mechanics and Geotechnical Engineering (Elsevier) and a regular reviewer for several leading journals in the fields of mining engineering, geotechnical engineering, and applied artificial intelligence.