Paul Wu

Paul Wu is an Associate Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science, where he also serves as an industry research fellow in the strategic partnership between the Centre for Data Science (CDS) and AIS/QAS. He leads the sports systems domain within CDS and applies statistical and machine learning models to complex systems across sports, marine science, and defence sectors.

• PhD, Queensland University of Technology
• Master of Engineering Science (Computer and Comm Engineering), Queensland University of Technology
• Bachelor of Engineering (Electrical and Computer Engineering), Queensland University of Technology

His research focuses on Bayesian statistics, dynamic Bayesian networks, state space modelling, and simulation techniques. He works closely with domain experts to solve real-world problems in sports performance, ecological resilience, and human systems. His interdisciplinary work spans sports science, marine ecology, and defence applications.

Paul’s recent publications demonstrate a strong trajectory in predictive analytics for elite sports and ecosystem modelling, particularly using Bayesian frameworks. His work on swimming performance prediction has informed national training strategies and contributed to competitive success. He has also advanced methods in clustering, model adaptation, and psychosocial risk assessment.

His scientific contributions have been recognized through impactful collaborations with elite sports organizations including the Australian Institute of Sport, Swimming Australia, and the West Coast Eagles. Testimonials highlight his role in transforming data analytics in sports injury recovery and performance optimization.

• Applied Bayesian models in elite sports decision-making
• Developed predictive tools for marine ecosystem resilience
• Collaborated on over 30 industry and government projects
• Supervises research in complex sports data analytics

Paul leads a dynamic research environment focused on translating statistical innovation into practical impact across diverse domains.