Feng Du

Feng Du is an Associate Professor and Doctoral Supervisor at China University of Mining and Technology-Beijing. His research focuses on the mechanisms, prevention and control, and AI-enabled early warning of deep coal-gas compound dynamic disasters.He has systematically investigated the progressive instability, energy evolution, and damage-seepage coupling behavior of gas-bearing coal-rock composite systems under complex stress conditions. His work has revealed the multi-field coupling mechanisms underlying coal-gas compound dynamic disasters and clarified the coordinated prevention mechanism involving pressure-relief energy dissipation and medium modification. He has developed targeted technologies and equipment for the precise prevention and control of dynamic disasters under different geological conditions. He has also introduced deep learning and multi-source information fusion into mine disaster prediction, developing intelligent early-warning models and visualization platforms that improve the accuracy and timeliness of risk identification.He has led 15 publicly funded research projects supported by the National Natural Science Foundation of China, the National Key Research and Development Program of China, the National Science and Technology Major Project, and the China Postdoctoral Science Foundation. He has published 107 academic papers, including 61 as first or corresponding author, with two ESI Hot Papers and five Highly Cited Papers. In 2025, his work received the First Prize of the Science and Technology Award of the China Occupational Safety and Health Association. As the first-ranked contributor, he received the Second Prize of the Safety Science and Technology Progress Award of the China Association of Work Safety and the Third Prize of the Science and Technology Award of the China National Coal Association. He was also selected for the Young Talent Support Programs of the China Association for Science and Technology and the Beijing Association for Science and Technology and recognized as a CNKI Highly Cited Scholar in the top 1%.