Zeng Fan

Zeng Fan is a Research Fellow at Nottingham Business School, Nottingham Trent University, working within the Centre for Business and Innovation Technology (CBIT). He serves as a co-module leader for the Global Consultancy module, contributing to academic programs at the business school.

His research spans Artificial Intelligence, Machine Learning, and Data Science with deep expertise in big data analysis, optimization, feature/event detection, and predictive modeling. Previously at Brunel University London, he developed a smart scheduling framework for mining operations optimizing resource allocation and production efficiency. At London South Bank University's Sustainable Innovation Centre, he specialized in signal processing, IoT, and acoustics while facilitating clean technology commercialization for SMEs like Measurable Energy and COYOSY.

Dr. Fan's work bridges technical innovation with sustainable business applications, having assisted 11 enterprises in commercializing clean technology solutions through university-SME partnerships. His methodology combines supervised and unsupervised learning approaches to eliminate data redundancy and enhance analytical precision in large-scale operational environments.