My research is in computational statistics. In particular, I enjoy working to try and solve "intractable" (computationally or analytically) statistical problems. This covers many areas, some of which are Bayesian statistics, big data, computational techniques and simulation-based algorithms, machine learning, statistical extreme value theory (with environmental applications) and I cover a diverse range of scientific applications.
Bayesian Methods: Bayesian statistics represents uncertainty about observed data, model parameters and even the model through probability distributions. As a consequence, this uncertainty is naturally incorporated into any statistical inference. Interest in Bayesian methods has exploded since the early 1990s thanks to the development of cheap computational power.
Big Data: As the size of datasets requiring analysis increases, so must the statistical techniques used to analyse them be able to efficiently handle the increase in scale. Standard statistical approaches, both classical and Bayesian, were not designed with this in mind. We are interested in developing ways to improve existing statistical techniques, as well as developing completely new methods, to better cope with big data.
Computational Statistics and Algorithms: Bayesian statistics depends on sophisticated simulation algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and many others. We are interested in developing new and more efficient algorithms in general and specific cases. We are also interested in developing new statistical approaches for models with computationally intractable likelihood functions, such as Bayesian "likelihood-free" methods (e.g. approximate Bayesian computation), indirect inference and composite likelihoods.
Extreme Value Theory: Extreme value theory involves the construction of mathematical models that describe the extreme levels of real world processes. Our focus is the development of new spatial and temporal models for extremes (such as max-stable and max-infinitely-divisible models), and new statistical procedures for fitting them, as many models for extremes tend to be difficult to work with computationally. We are particularly interested in the extremes of climate and environmental processes.
Diverse Applications: We are interested in using Bayesian methods to solve challenging scientific problems in a broad range of disciplines. These include biology, climate science, ecology, hydrology and population genetics among others. We always work closely with experts in each field to obtain the best possible results. Our applied research occasionally generates press coverage.
Project supervision is available in all of the above research areas.
Understand what you enjoy doing, and a broad idea of the kind of thing that you would like to do. Then find the right people to make it happen!
These vary each session.
Associate Editor: Australia and New Zealand Journal of Statistics
Associate Editor: Statistics and Computing
Past President of the Australasian Society of Bayesian Analysis (a local chapter of ISBA)
Past President of the Statistical Society of Australia
Past President of the NSW Branch of the Statistical Society of Australia
2020 Service Award (Statistical Society of Australia)
2017 ARC Future Fellowship
2015 G. N. Alexander Medal, joint with F. Zheng, S. Westra and M. Leonard (Institution of Engineers Australia)
2011 Moran Medal, joint with Dr. M. M. Tanaka (Australian Academy of Science)
2010 J. G. Russell Award (Australian Academy of Science)
2010 Queen Elizabeth II Research Fellowship (Australian Research Council)
2006 John Yu Fellowship to Europe (Imperial College, London)
2005 UNSW Faculty of Science Learning and Teaching Award