Mark Cooper

Professor Mark Cooper is Chair of Prediction Based Crop Improvement at The University of Queensland, and a global leader in quantitative genetics and plant breeding. His work involves integrating genomic prediction and crop growth models into an 'end to end' framework for crop improvement. Professor Cooper has pioneered the development of novel genetic modelling methodologies, based on gene networks, to study important properties of quantitative traits in biology, and demonstrated how this new genetic modelling framework can be successfully used in plant breeding to improve prediction of important traits under the influences of selection. Professor Cooper's work at DuPont Pioneer on drought adaptation in one of the largest maize breeding programs in the world led to the AQUAmax hybrids that presently cover millions of hectares worldwide. A quantitative geneticist by training, Professor Cooper spent 20 years working with industry in the United States and as CEO of his own consultancy firm Zenrun42, before returning to UQ to build upon the critical mass of predictive agricultural expertise in QAAFI and the wider university. Quantitative genetics, plant breeding, genomic prediction, crop growth models, genetic modelling methodologies, gene networks, drought adaptation in maize, predictive agriculture

[2026] The hidden geometry of breeding constraints. Nature Plants, 12 (7), 1316-1324. doi: 10.1038/s41477-026-02332-6 [2026] Clustering compresses attractors in Watts–Strogatz threshold Boolean networks. Journal of Complex Networks, 14 (4) cnag027, 4. doi: 10.1093/comnet/cnag027 [2026] Ensembles of Graph Attention Networks Supervised by Genotype-to-Phenotype Structures Improved Genomic Prediction Performance. in silico Plants, 8 (1) diag014, 1. doi: 10.1093/insilicoplants/diag014 [2026] Better breeding leveraging more biology. Trends in Plant Science. doi: 10.1016/j.tplants.2026.05.004 [2026] Biology-driven genebank pangenetics meet AI-powered predictions to transform crop breeding. Molecular Plant, 19 (6), 1140-1143. doi: 10.1016/j.molp.2026.03.017 [2026] Optimisation of weighted ensemble