Dr. John T. Ormerod is an outstanding young statistician whose principal research focus is in the exciting field of probabilistic machine learning. This class of methods synergize the probabilistic and theoretical foundations of Statistics with the fast approximate inference algorithms of Machine Learning. John’s methodologies are typically hundreds to thousands of times faster than existing methods, allowing successful application in numerous fields, and in particular on Big Data problems. John’s research methodologies have been used in diverse areas including meteorology, neuroscience, psychology, and ecology.
Variational Inference Methods
Bachelor of Science (Honours 1) 2003.The University of New South Wales, Australia.
Masters of Statistics (Coursework) 2004. The University of New South Wales, Australia.
Ph.D. in Statistics 2008. The University of New South Wales, Australia.
Strbenac, D., Mann G. J., Ormerod J. T., & Yang J. Y. H.
(2015). ClassifyR: an R package for performance assessment of classification with applications to transcriptomics. Bioinformatics. 31(11), 1851-1853. doi: 10.1093/bioinformatics/btv066