Dr. Qing Du | Multimodal Algorithm | AI & Machine Learning Award
Dr. Qing Du | University of South China | China
Dr. Qing Du is a dedicated doctoral researcher in Mining Engineering at the University of South China, specializing in intelligent monitoring and early-warning technologies for deep underground engineering safety. Under the mentorship of Professor Yang Shijiao, she has combined expertise in artificial intelligence, multimodal algorithms, and engineering safety to address challenges in subsurface environments. Her innovative research focuses on integrating advanced deep-learning methods with physics-guided modeling to improve underground hazard detection and prediction. Through her leadership in research projects and impactful publications, she has established herself as an emerging expert in intelligent mining safety systems.
Professional Profile
SCOPUS
Summary of Suitability
Dr. Qing Du is an exceptionally talented and accomplished young female researcher in the field of intelligent monitoring, deep underground engineering safety, and artificial intelligence multimodal algorithms. Currently pursuing her Doctoral degree in Mining Engineering at the University of South China, under the supervision of Professor Yang Shijiao, she has demonstrated outstanding research skills and innovative thinking. With multiple high-impact publications in top SCI journals, successful leadership of research projects, and numerous prestigious awards, Dr. Du Qing stands out as a highly promising and deserving candidate for the Best Researcher Award.
Education
Dr. Qing Du earned her bachelor’s degree in Measurement and Control Technology and Instruments from the Hunan Institute of Technology, School of Electrical and Information Engineering. She later joined the School of Resources, Environment, and Safety Engineering at the University of South China, where she is currently pursuing a combined master’s and doctoral program in Mining Engineering. Throughout her academic journey, she has focused on leveraging artificial intelligence and multimodal data analytics to improve underground monitoring, hazard detection, and real-time safety assessments.
Experience
Dr. Qing Du has accumulated significant research experience through her involvement in multiple funded projects and scholarly collaborations. As the principal investigator for several Hunan Provincial Graduate Research Innovation Projects, she has led work on monitoring video image processing in complex mining environments and conducted multimodal experimental studies on rockburst tendencies during rock mass failure. These projects demonstrate her ability to combine theoretical modeling with practical engineering solutions. Additionally, she has contributed to the development of deep-learning-based intelligent detection frameworks, lightweight computer vision models, and numerical simulation-driven predictive systems for underground engineering safety.
Research Interests
Dr. Qing Du’s primary research interests lie at the intersection of artificial intelligence, multimodal deep learning, and underground engineering safety. Her focus includes developing robust detection systems that integrate physics-guided modeling with image enhancement techniques for low-light environments, constructing efficient real-time object detection algorithms for safety monitoring, and designing predictive models for tunnel deformation and rock brittleness analysis. By combining AI-powered algorithms with engineering expertise, she aims to create intelligent early-warning platforms capable of addressing complex underground safety challenges.
Awards
Dr. Qing Du has achieved significant recognition for her outstanding research and innovation in artificial intelligence and underground engineering safety. She has received the First Prize in the Hunan Provincial Graduate Artificial Intelligence Innovation Competition and the Second Prize in the Hunan Provincial Graduate Computer Innovation Competition, demonstrating her strong capabilities in intelligent system development. She was also awarded the Third Prize at the Hunan Provincial Graduate Innovation Forum and earned the Winning Award in the Graduate Innovation and Entrepreneurship Simulation Competition. In addition, she secured the Third Prize in both the Hunan Provincial Graduate Computer Innovation Competition and the Hunan Provincial Graduate Artificial Intelligence Innovation Competition, further highlighting her technical excellence and creativity. Her exceptional academic performance and research contributions were also recognized with the prestigious National Scholarship for Doctoral Students, underscoring her position as a leading young researcher in her field.
Publication Top Notes
Physics-guided multimodal deep learning reveals determinants of rock brittleness across scales
A hybrid zero-reference and dehazing network for joint low-light underground image enhancement
SCB-YOLOv5: a lightweight intelligent detection model for athletes’ normative movements
Intelligent detection method for underground mine workers wearing safety helmets
Large-scale numerical simulation-driven ensemble model for underground tunnel deformation prediction
Conclusion
Dr. Qing Du is an accomplished and promising young researcher in the field of intelligent mining safety systems and multimodal AI algorithms. Through her pioneering work on deep-learning-driven underground monitoring technologies, she has demonstrated the potential to transform mining engineering safety practices. Her high-impact publications, leadership in funded research projects, and success in competitive innovation awards showcase her ability to bridge cutting-edge artificial intelligence with real-world engineering solutions. As an emerging expert, her contributions are driving advancements in underground hazard prediction, intelligent early-warning systems, and AI-powered safety monitoring, making her a strong candidate for prestigious research awards and recognition in the field.