Shu-Kay Angus Ng

Professor Shu-Kay Angus Ng is a Professor of Biostatistics at Griffith University's School of Medicine and Dentistry, where he has been a faculty member since January 2018. He is an active researcher with the Centre for Applied Health Economics (CAHE) and serves as an Associate Editor for the Journal of Statistical Computation and Simulation. Professor Ng earned his PhD in Statistics from the University of Queensland in 1999 and has since established an international reputation in statistical methodology.

His research expertise spans both theoretical and applied statistics:

• Mixture models and cluster analysis for complex data
• Random effects modeling for correlated data structures
• Machine learning applications in medical contexts
• Survival analysis and longitudinal data methods
• Image analysis and pattern recognition techniques

Professor Ng's work addresses critical challenges across multiple health domains including oncology, comorbidity research, medical imaging, and health economics. His methodological contributions have significantly advanced evidence-based practice in medical and health research.

With over 160 publications accumulating more than 9,000 citations (H-index of 37), Professor Ng's scholarly impact is substantial. His 2019 monograph 'Mixture Modelling for Medical and Health Sciences' represents a landmark contribution to the field. He has secured significant research funding, including seven ARC Project grants and participation in the NHMRC Centre of Research Excellence in Better Health Outcomes for Compensable Injury.

Professor Ng's major recognitions include:

• Membership in the ARC College of Experts (2022-2025)
• Editorial role at the Journal of Statistical Computation and Simulation
• Successful leadership of multiple ARC and NHMRC-funded projects
• Extensive citation record reflecting scholarly influence

As an educator and mentor, Professor Ng has supervised five PhD students to completion since 2017 while currently guiding four additional doctoral candidates. His supervision spans diverse topics from comorbidity patterns to advanced statistical techniques for big data analysis in health contexts. He contributes to research training through professional advice on study designs, randomized controlled trials, and analytical strategies across multiple health disciplines.