BA (Hons) (Mathematical Statistics and Computing), PhD (Statistics) (UNE); GradDipHigherEd (QUT); FAAS, FASSA, QAAS
Econometric and statistical methods

The discipline of statistics sits at the interface of mathematics, data science and evidential inference in almost every field in the social sciences. My research contributes to this interface, by building statistical models and algorithms to extract information from data and other information in order to address general and specific problems in health, society and the environment.

The impact of my work is demonstrated not only by the uptake of the new methodology in the statistical research community, but also by the uptake of new statistical and machine learning methods by social science researchers, as well as changes in government policies and business processes. An example of the latter is an increase in travel subsidies for rural cancer patients to access treatment, based on collaborative work with the Cancer Council of Queensland.

Importantly, almost all of my work is undertaken as part of a team comprising researchers with different core and applied skills, and practitioners in the field of application. This creates the pathway from new research solutions to practical translation of this research and thence to impact. For example, my work on monitoring infection in hospitals was undertaken in close collaboration with medical practitioners in order to make sure that the right question were asked and answered, and that the statistical models developed to predict outbreaks of infection were implemented in the hospital. Similarly, my work in conservation in Australia, Southern Africa, Indonesia and South America could not have had impact on government awareness without close collaboration with ecologists, conservation groups and government representatives in those places. Close collaboration with social scientists has also been an essential part of my work on industry projects with airports, energy and urban development. Indeed, my research is much more fruitful, multi-dimensional, inspirational and fun because of these collaborations.

The emergence of new technology and new forms of data is also creating great opportunities for statisticians to have significant impact in the social sciences. My recent work has involved thinking about how we collect, analyse and communicate these data to better address real-world problems. Research into the use of virtual reality to elicit information from experts, the analysis of data from drones and satellites, and the interpretation of data from wearables and sensors, has started to have impact in the areas of conservation, medical imaging, agriculture, official statistics, health and sports. Again, this can only be accomplished through teams with diverse expertise, and through thinking about innovative ways to collect, analyse and communicate these data. For example, one of our recent projects on developing better ways to monitor the Great Barrier Reef has included developing a virtual online reef that allows divers and other groups to geo-tag their underwater images; we can then use statistical methods and citizen science to extract information from these images and incorporate this information to improve our understanding and predictions about the health of the reef.

Fellow of the Australian Academy of Science

Fellow of the Queensland Academy of the Arts and Sciences

Fellow of the Academy of Social Sciences in Australia

  1. Wu, PPY, Mengersen K, McMahon K, et al. 2017 Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience. Nature Communication 8(1), 1263.
  2. Cramb, SM, Moraga, P, Mengersen KL, Baade, PD (2017) Spatial variation in cancer incidence and survival over time across Queensland, Australia. Spatial and Spatio-Temporal Epidemiology 23, 59.
  3. Abram, NK, Meijaard, E, Wilson, KA, et al. (2017) Oil palm community conflict mapping in Indonesia: A case for better community liaison in planning for development initiatives. Applied Geography 78, 33.
  4. Drovandi, C, Holmes, C, McGree, JM, et al. (2017) Principles of experimental design for Big Data analysis. Statistical Science 32, 385.
  5. Brown, R, Bruza, P, Heard, W, et al. (2016) On the (virtual) getting of wisdom: Immersive 3D interfaces for eliciting spatial information from experts. Spatial Statistics 18, 318.