Dr Chris Drovandi is an Associate Professor in the School of Mathematical Sciences at the Queensland University of Technology (QUT) and is an Associate Investigator of ACEMS. From 2016-2018 Chris held an Australian Research Council Discovery Early Career Researcher's Award, a highly competitve Government research grant. He is the Chair of the Bayesian Statistics Section of the Statistical Society of Australia and is an Associate Editor of Statistics and Computing. His research interests are in Bayesian algorithms for complex models, optimal Bayesian experimental design methods and the translation of Bayesian methods across many disciplines.
Everybody is different, and every body is different. Significant variability is a common feature of all of the physiological systems that compose the function of the human body, and understanding this variability is critical to explaining differences in susceptibility to pathological conditions, and also to explaining how medical treatments can potentially succeed or fail even when applied to treat the same condition.
Ryan, C. M., Drovandi C. C., & Pettitt A.N.
(2016). Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models. Bayesian Analysis. 11(3), 857-883. doi: 10.1214/15-BA977
Drovandi, C. C., Pettitt A.N., & McCutchan R. A.
(2016). Exact and approximate Bayesian inference for low integer-valued time series models with intractable likelihoods. Bayesian Analysis. 11(2), 325-352. doi: 10.1214/15-BA950
Ali, H., Cameron E., Drovandi C. C., McCaw J. M., Guy R. J., Middleton M., et al.
(2015). A new approach to estimating trends in chlamydia incidence. Sexually Transmitted Infections. 91(7), 513-519. doi: 10.1136/sextrans-2014-051631