Braden completed his PhD in applied mathematics at the University of Western Australia. His work focused on the application of dynamical system methods to nonlinear time series analysis and machine learning tasks, with application to asset maintenance within the mining sector through the ITTC Centre for Transforming Maintenance Through Data Science.
As a postdoctoral research fellow within AAGI, Braden is looking to apply his expertise with time series and complex data – such as video and image data – to tackle novel and important problems within the Australian grains sector.