David Broadhurst

David Broadhurst

Professor David Broadhurst (www.davidbroadhurst.net), born Chester UK, holds a first class honours degree in Electronic Engineering, an MSc in Medical Informatics, and a PhD on the subject of ‘‘Application of Artificial Neural Networks and Evolutionary Algorithms to Metabolic Profiling’’. He has been an active member of the metabolomics community for the last 18 years, where he is a recognized expert in design of experiments, signal processing, biostatistics, and machine learning. David worked for an extended period as a post-doctoral research fellow developing large-scale clinical metabolomics protocols alongside Dr. Warrick Dunn, at the University of Manchester as part of Professor Douglas Kell’s Bioanalytical Sciences Group. In 2009 he moved to Cork University Maternity Hospital, Ireland, to investigate pre-symptomatic metabolite biomarkers predictive of major pregnancy diseases. In 2011 David was appointed Associate Professor of Biostatistics at the University of Alberta, Canada, where he was scientific lead for a range of basic/clinical metabolomics projects, and continued his pregnancy related research. More recently he has expanded his research portfolio to a diverse range of post-genomic translational/precision medicine projects. In March 2016 he was appointed to his current position as Professor at Edith Cowan University, Perth, Australia. In addition to many collaborative Systems Biology projects. His current research primarily focuses the application and optimization of diverse multivariate modelling techniques (parametric & non-parametric linear models, decision trees, machine learning, Structural Equation Modelling, Multilevel random-effects models, etc.) within the domain of systems-biology. Additionally, he has research interests in data visualization, design of experiments, and developing quality assurance procedures for ‘omic based studies. David travels extensively around the globe lecturing on the perils of poor experimental design and importance of robust and diverse statistical analysis.