Chris Drovandi | ARC Centre of Excellence for Mathematical and Statistical Frontiers

Associate Investigator

Queensland University of Technology

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.

Research Interests:

Applied statistics

Bayesian computational algorithms

Bayesian design

Bayesian inference

Bayesian methodolgy

Bayesian statistical computation

Bayesian statistical modelling

Computational statistics

Qualifications:

Doctor of Philosophy (Statistics)

Projects

Safer Drug Screening and Better Simulations: Maths Getting to the Heart of the Matter

More accurately simulating how the heart functions and screening for side-effects of new drugs are just two results that have sprung from a collaboration between Australian and British scientists.

Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

ACEMS researchers are continuing to build off their research into computationally efficient simulation-based parameter estimation methods for complex models.

Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration

The methods presented in this paper are useful for farmers and landowners in estimating carbon stocks in agricultural production systems.

Efficient Bayesian Synthetic Likelihood with Whitening Transformations

ACEMS researchers introduce a new method which significantly improves the computational efficiency of Bayesian synthetic likelihood.

From Cells to Organs: Exploring Variability in Physiological Systems

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.

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Using Bayesian statistics and mathematical models to assess the global response to COVID-19. This analysis uses data from 158-countries.

Particle Methods for Stochastic Differential Equation Mixed Effects Models

ACEMS Researchers at QUT and UNSW introduce three novel parameter estimation methods for stochastic differential equation mixed effects models (SDEMEMs).

Robust Approximate Bayesian Inference with Synthetic Likelihood

ACEMS researchers develop a novel statistical approach to the parameter estimation of complex models.

Publications

Invited talks, refereed proceedings and other conference outputs

2017

Cespedes, M. I., McGree J., Drovandi C. C., Mengersen KL., Doecke J., & Fripp J. (2017). Spatio-temporal cortical brain patterns of Alzheimer's disease. The International Biometric Society Australasian Region Conference.

Cespedes, M. I., McGree J., Drovandi C. C., Mengersen KL., Doecke J., & Fripp J. (2017). Spatio-temporal brain cortical patterns of Alzheimer's disease. Bayes on the Beach.

Ebert, A., Wu P., Dutta R., Mengersen KL., Ruggeri F., Mira A., et al. (2017). Approximate Bayesian Computation for Dynamic Queueing Networks. 5th Symposium on Games and Decisions in Reliability and Risk.

2016

Mengersen, KL., Clifford S., Drovandi C. C., Harden F., Harden M., & Tierney N J. (2016). Bayesian Approaches in Occupational Health Surveillance. International Society for Bayesian Analysis.

Drovandi, C. C. (2016). Bayesian Synthetic Likelihood. Markov chain and Quasi-Monte Carlo Methods.

Vo, B., Drovandi C. C., Pettet G., & Pettitt A.N. (2016). Approximate Bayesian Computation for cell biology. Sixth IMS-ISBA Joint Meeting Bayes Comp at MCMski 5.

Journal Articles

2019

An, Z., L. F. South, Nott D. J., & Drovandi C. C. (2019). Accelerating Bayesian Synthetic Likelihood with the Graphical Lasso. Journal of Computational and Graphical Statistics. 28(2), 471-475. doi: 10.1080/10618600.2018.1537928

2018

Lawson, B. A. J., Drovandi C. C., Cusimano N., Burrage P., Rodriguez B., & Burrage K. (2018). Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology. Science Advances. 4(1), e1701676. doi: 10.1126/sciadv.1701676

Dehideniya, M. B., Drovandi C. C., & McGree J. (2018). Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Computational Statistics & Data Analysis. 124, 277 - 297. doi: 10.1016/j.csda.2018.03.004

Ong, V. M. H., Nott D. J., Tran M N., Sisson S., & Drovandi C. C. (2018). Variational Bayes with synthetic likelihood. Statistics and Computing. 28(4), 971-988. doi: 10.1007/s11222-017-9773-3

L. F. South, Drovandi C. C., Lee A.., & Nott D.. J. (2018). Bayesian Synthetic Likelihood. Journal of Computational and Graphical Statistics. 27(1), 1-11. doi: 10.1080/10618600.2017.1302882

L. F. South, Mira A., & Drovandi C. C. (2018). Regularised Zero-Variance Control Variates. arXiv. arXiv:1811.05073v1.

Ong, V. M. - H., Nott D. J., Tran M N., Sisson S., & Drovandi C. C. (2018). Likelihood-free inference in high dimensions with synthetic likelihood. Computational Statistics & Data Analysis. 128, 271 - 291. doi: 10.1016/j.csda.2018.07.008

Drovandi, C. C., & Tran M N. (2018). Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo. Bayesian Analysis. 13(1), 139–162. doi: 10.1214/16-BA1045

Lawson, B. A. J., Burrage K., Burrage P., Drovandi C. C., & Bueno-Orovio A. (2018). Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation. Frontiers in Physiology. 9, 1114. doi: 10.3389/fphys.2018.01114

Salomone, R., L. F. South, Drovandi C. C., & Kroese D. (2018). Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. arXiv. arXiv:1810.12499.

Overstall, A. M., McGree J., & Drovandi C. C. (2018). An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing. 28(2), 343-358. doi: 10.1007/s11222-017-9734-x

Drovandi, C. C., Moores M., & Boys R. J. (2018). Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics & Data Analysis. 118, 1-17. doi: 10.1016/j.csda.2017.09.002

Cespedes, M. I., McGree J., Drovandi C. C., Mengersen KL., Doecke J. D., Fripp J., et al. (2018). An efficient algorithm for estimating brain covariance networks. PLOS ONE. 13(7), e0198583. doi: 10.1371/journal.pone.0198583

2017

Cespedes, M. I., Fripp J., McGree J., Drovandi C. C., Mengersen KL., & Doecke J. D. (2017). Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference. BMJ Open. 7(2), 12174-12174. doi: 10.1136/bmjopen-2016-012174

Drovandi, C. C., Holmes C. C., McGree J., Mengersen KL., Richardson S., & Ryan E. G. (2017). Principles of Experimental Design for Big Data Analysis. Statistical Science. 32(3), 385 - 404. doi: 10.1214/16-STS604

Chen, C.C.-M.., Drovandi C. C., Keith J.M.., Anthony K., M. Caley J., & Mengersen KL. (2017). Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef. Ecological Modelling. 364, 113 - 123. doi: 10.1016/j.ecolmodel.2017.09.006

Chen, CC-M., Bourne D. G., Drovandi C. C., Mengersen KL., Willis B. L., M. Caley J., et al. (2017). Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora. PeerJ. 5, e3438. doi: 10.7717/peerj.3438

L. F. South, Drovandi C. C., & Pettitt A.N. (2017). Discussion of: A Bayesian information criterion for singular models. Journal of the Royal Statistical Society, Series B (Statistical Methodology).

2016

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

Ryan, E. G., Drovandi C. C., McGree J., & Pettitt A.N. (2016). A Review of Modern Computational Algorithms for Bayesian Optimal Design. International Statistical Review. 84(1), 128-154. doi: 10.1111/insr.12107

Kang, S. Y., McGree J., Drovandi C. C., M. Caley J., & Mengersen KL. (2016). Bayesian adaptive design: Improving the effectiveness of monitoring of the Great Barrier Reef. Ecological Applications. 26(8), 2637-2648. doi: 10.1002/eap.1409

Mengersen, KL., Drovandi C. C., Robert C. P., Pyne D. B., Gore C. J., & Chen C. W. S. (2016). Bayesian estimation of small effects in exercise and sports science. PLOS ONE. 11(4), 1-23. doi: 10.1371/journal.pone.0147311

Drovandi, C. C., Cusimano N., Psaltis S. T. P., Lawson B. A. J., Pettitt A.N., Burrage P., et al. (2016). Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments. Journal of The Royal Society Interface. 13(121), doi: 10.1098/rsif.2016.0214

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

2015

Lee, X. J., Drovandi C. C., & Pettitt A.N. (2015). Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets. Biometrics. 71(1), 198-207. doi: 10.1111/biom.12249

Vo, B., Drovandi C. C., Pettitt A.N., & Simpson M. J. (2015). Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation. Mathematical Biosciences. 263, 133-142. doi: 10.1016/j.mbs.2015.02.010

Drovandi, C. C., Pettitt A.N., & Lee A. (2015). Bayesian Indirect Inference Using a Parametric Auxiliary Model. Statistical Science. 30(1), 72-95. doi: 10.1214/14-STS498

Vo, B., Drovandi C. C., Pettitt A.N., Pettet G., & Byrne H. (2015). Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation. PLoS Computational Biology. 11(12), doi: 10.1371/journal.pcbi.1004635

Ryan, E. G., Drovandi C. C., & Pettitt A.N. (2015). Simulation-based fully Bayesian experimental design for mixed effects models. Computational Statistics & Data Analysis. 92, 26-39. doi: 10.1016/j.csda.2015.06.007

McGree, J., Drovandi C. C., White G., & Pettitt A.N. (2015). A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing. 26(5), 1121-1136. doi: 10.1007/s11222-015-9596-z

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

Ryan, E., Drovandi C. C., & Pettitt A.N. (2015). Fully Bayesian Experimental Design for Pharmacokinetic Studies. Entropy. 17(3), 1063-1089. doi: 10.3390/e17031063

Xueou, W., Nott D. J., Drovandi C. C., Mengersen KL., & Evans M. (2016). Using history matching for prior choice.

Research links

ORCID

Contact

c.drovandi@qut.edu.au

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Assoc. Professor Chris Drovandi

Projects

Safer Drug Screening and Better Simulations: Maths Getting to the Heart of the Matter

Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration

Efficient Bayesian Synthetic Likelihood with Whitening Transformations

From Cells to Organs: Exploring Variability in Physiological Systems

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Particle Methods for Stochastic Differential Equation Mixed Effects Models

Robust Approximate Bayesian Inference with Synthetic Likelihood

Publications

2017

2016

2019

2018

2017

2016

2015

2014

2017

2016

Research links

Contact

Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Assoc. Professor Chris Drovandi

Subscribe to our newsletter

Stay connected

Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers