Rate My Professor Brett Ninness

Professor Brett Ninness is a Professor in the School of Electrical Engineering and Computing, part of the School of Engineering at the University of Newcastle, Australia. Born in Singleton, Australia, he received his Bachelor of Engineering (BE), Master of Engineering (ME), and PhD degrees in Electrical Engineering from the University of Newcastle. He has held key administrative positions at the institution, including Assistant Dean (Research Training) for three years, Deputy Head of Faculty, and Acting Pro Vice-Chancellor. Throughout his career, he has supervised 11 higher degree by research students to completion, covering topics such as blind identification of continuous-time LTI systems, computational Bayesian methods for communications and control, and algorithms for multi-antenna receivers.

His research specializations encompass dynamic system modelling, system identification, and stochastic signal processing, fields in which he has authored over 100 papers. Selected key publications include the chapter 'System Identification Software' in multiple works (2014, 2015, 2021), 'System identification of linear parameter varying state-space models' (2011), 'Variance Error, Reproducing Kernels, and Orthonormal Bases' (2005), and conference papers such as 'A Numerically Robust Bayesian Filtering Algorithm for Gaussian Mixture Models' (2023), 'Variational State and Parameter Estimation' (2021), and 'Smoothed State Estimation via Efficient Solution of Linear Equations' (2017). His scholarship has earned over 7,900 citations on Google Scholar. Professor Ninness has contributed editorially as Associate Editor for Automatica and IEEE Transactions on Automatic Control, and Editor-in-Chief of IET Control Theory and Applications. He chaired the International Federation of Automatic Control (IFAC) Technical Committee on Modelling, Identification and Signal Processing, the Institute of Electrical and Electronics Engineers (IEEE) Technical Committee on System Identification and Adaptive Control, and served on the Australian Research Council College of Experts. Major awards include the Tall Poppy Award (2001) and IEEE Fellowship (2017) for computational methods in system identification. He has also secured multiple Australian Research Council grants for projects on nonlinear identification, orthonormal basis functions, and advanced control applications.