Brian Cullis

Senior Professor Brian Cullis is a statistician in the School of Mathematics and Applied Statistics at the University of Wollongong (UOW). He joined UOW in 2011 as the inaugural chair in Biometry, a position jointly funded by the Grains Research and Development Corporation (GRDC). Previously, he worked at the NSW Department of Primary Industries (NSWDPI) from 1978 to 2010, where he developed expertise in applied statistics for bio-sciences and advanced to Senior Principal Research Scientist.

• Education: BSc(Hons) in Mathematical Statistics (1977, University of Sydney); PhD (1991, University of New South Wales)

Research Interests: Professor Cullis specializes in linear mixed models, comparative experimental design, and genomic data analysis. His work focuses on methodologies for multi-environment plant breeding trials, optimal experimental designs, and one-stage genomic analysis algorithms. Current projects include integrating environmental covariates into mixed model frameworks and advancing genomic selection in crops.

Article Trends: His recent publications (2020–2025) emphasize plant breeding, statistical modeling of genotype-environment interactions, and applications of linear mixed models in agricultural genomics. Key subfields include crop improvement under environmental stress, spatial trial analysis, and genomic resistance mapping in wheat and canola.

Scientific Awards: He has secured substantial GRDC funding for statistical support in plant improvement, including projects like 'Statistics for the Australian Grains Industry' ($6 million) and capacity-building initiatives. Additional grants include ARC Training Centre in Predictive Breeding (2024–2029) and University of Adelaide subcontracts for wheat LMA risk screening.

Supervision: Active in HDR supervision, he currently guides research on machine learning for variety interactions, factorial experiment design, and single-step genomic selection models. Completed projects include computational methods for factor analytic models and information diagnostics for multi-environment datasets.

Collaboration Network: His work intersects with marine ecology (e.g., SMART drumline effectiveness) and socioeconomics, reflecting interdisciplinary leadership in statistical applications.