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Professor Chris Drovandi
Faculty of Science,
School of Mathematical Sciences
Biography
Dr Chris Drovandi is a Professor in the School of Mathematical Sciences at the Queensland University of Technology (QUT). Chris completed his PhD in 2012 at QUT and currently holds an Australian Research Council (ARC) Future Fellowship on statistical inference for implicit models and an ARC Discovery Project on novel Sequential Monte Carlo methods. From 2016-2019 he held an ARC Discovery Early Career Researcher Fellowship. He is currently an Associate Editor for Statistics & Computing and the SIAM/ASA Journal of Uncertainty Quantification. His research interests are in Bayesian algorithms for complex models, optimal Bayesian experimental design methods and the translation of Bayesian methods across many disciplines. Chris is currently supervising several post-graduate students. For more information, visit Dr Drovandi’s website.Personal details
Positions
- ARC Future Fellow
Faculty of Science,
School of Mathematical Sciences - Professor
Faculty of Science,
School of Mathematical Sciences
Keywords
Approximate Bayesian Computation, Markov chain Monte Carlo, Sequential Monte Carlo, Statistics
Research field
Statistics
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2020
Qualifications
- PhD(Statistics) (Queensland University of Technology)
- BAppSc(Statistics) (Queensland University of Technology)
- BInfTech(SoftwareEng/DataComms) (Queensland University of Technology)
Professional memberships and associations
- Program Director of the Models and Algorithms Program of the QUT Centre for Data Science
- Associate Editor, Statistics and Computing, Springer
- Associate Editor, SIAM/ASA Journal of Uncertainty Quantification
- 2016-2019 Chair, Bayes Section of the Statistical Society of Australia
- Member: International Society for Bayesian Analysis
- Member: Statistical Society of Australia
Teaching
Achievements in Teaching
- 2021 Director of the AMSI Winter School on Statistical Data Science
- 2017 AMSI Winter School lecturer by invitation (Queensland University of Technology)
- 2015 AMSI Summer School lecturer by invitation (University of Newcastle)
- Nominated for 2015 QUT Vice-Chancellor’s Award for Excellence in Teaching
- Nominated for 2014 QUT Science and Engineering Faculty Award for Excellence in Postgraduate Research Supervision
Postgraduate Teaching
- Multivariate distributions
- Classical statistical inference
- Bayesian statistical inference
- Monte Carlo Integration
- Importance Sampling and Adaptive Importance Sampling
- Markov chain Monte Carlo
- Sequential Monte Carlo
- Approximate Bayesian Computation
Undergraduate Teaching
Probability and Stochastic Modelling 2
- Generating Functions
- Transformation of Random Variables
- Order Statistics
- Markov Chains including Simple Random Walks and Branching Processes
- Continuous Time Markov Chains
- Brownian Motion
Probability and Stochastic Modelling 1
- Probability
- Univariate Discrete and Continuous Random Variables
- Bivariate Discrete Distributions
- Chi-square Goodness-of-fit
- Markov Chains
- Poisson Processes
Aspects of Computational Science
- Random Number Generation
- Rejection Sampling
- Importance Sampling
- Monte Carlo Integration
- Normal regression
- Poisson and binomial regression
- Multinomial regression
- log-linear models
- R Programming
Publications
- Monsalve-Bravo, G., Lawson, B., Drovandi, C., Burrage, K., Brown, K., Baker, C., Vollert, S., Mengersen, K., Mcdonald-Madden, E. & Adams, M. (2022). Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data. Science Advances, 8(38). https://eprints.qut.edu.au/234309
- Priddle, J., Sisson, S., Frazier, D., Turner, I. & Drovandi, C. (2022). Efficient Bayesian Synthetic Likelihood With Whitening Transformations. Journal of Computational and Graphical Statistics, 31(1), 50–63. https://eprints.qut.edu.au/233361
- Frazier, D. & Drovandi, C. (2021). Robust Approximate Bayesian Inference With Synthetic Likelihood. Journal of Computational and Graphical Statistics, 30(4), 958–976. https://eprints.qut.edu.au/233362
- Price, L., Drovandi, C., Lee, A. & Nott, D. (2018). Bayesian synthetic likelihood. Journal of Computational and Graphical Statistics, 27(1), 1–11. https://eprints.qut.edu.au/222949
- Lawson, B., Drovandi, 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). https://eprints.qut.edu.au/223413
- Drovandi, C., Pettitt, T. & Lee, A. (2015). Bayesian indirect inference using a parametric auxiliary model. Statistical Science, 30(1), 72–95. https://eprints.qut.edu.au/63767
- Drovandi, C. & Pettitt, T. (2011). Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics, 67(1), 225–233. https://eprints.qut.edu.au/39328
QUT ePrints
For more publications by Chris, explore their research in QUT ePrints (our digital repository).
Awards
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2021
- Details
- Moran Medalhttps://www.science.org.au/supporting-science/awards-and-opportunities/moran-medal
- Type
- Committee Role/Editor or Chair of an Academic Conference
- Reference year
- 2020
- Details
- Co-Chair of local organising committee for the Australian and New Zealand Statistics Conference 2020 (postponed due to COVID19)
- Type
- Editorial Role for an Academic Journal
- Reference year
- 2017
- Details
- Coordinating Editor for Statistics and Computing (within top 2 journals in the discipline of computational statistics and top 10% in SCImago Statistics & Probability category)
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2016
- Details
- Vice-Chancellor's Performance Fund Award (for excellence in Research)
- Type
- Other
- Reference year
- 2016
- Details
- Chair of the Bayesian Statistics section of the Statistical Society of Australia (2016-2019)
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2012
- Details
- The ISBA Lifetime Members Junior Researcher Award
Selected research projects
- Title
- Scalable and Robust Bayesian Inference for Implicit Statistical Models
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- FT210100260
- Start year
- 2022
- Keywords
- Title
- Advances in Sequential Monte Carlo Methods for Complex Bayesian Models
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP200102101
- Start year
- 2020
- Keywords
- Title
- Short and Long-Term Effects of Therapeutic Exercise in Children with Bronchiectasis: A Multi-Centre Randomised Controlled Trial
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- 1160613
- Start year
- 2019
- Keywords
- Bronchiectasis; Exercise Therapy; Child Health; Exacerbation; Quality of Life
- Title
- Tractable Bayesian Algorithms for Intractable Bayesian Problems
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DE160100741
- Start year
- 2016
- Keywords
Projects listed above are funded by Australian Competitive Grants. Projects funded from other sources are not listed due to confidentiality agreements.
Supervision
Completed supervisions (Doctorate)
- Advances in sequential Monte Carlo methods (2022)
- Bayesian Model Selection and Inference for Field Studies of Soil Carbon Cycling (2022)
- Statistical Modeling and Machine Learning in Longitudinal Data Analysis (2021)
- Contributions to Bayesian Synthetic Likelihood (2020)
- Experimental Design for Dependent Data (2020)
Completed supervisions (Masters by Research)
- Implementing Bayesian Synthetic Likelihood within the Engine for Likelihood-Free Inference (2022)
- A Novel Bayesian State-Space Model for Estimating Mosquito Populations (2021)
- Cost Effective Functional Response Experiments via Sequential Design (2021)
- Estimating Parameters of a Stochastic Cell Invasion Model with Flourescent Cell Cycle Labelling Using Approximate Bayesian Computation (2021)
- Efficient and Flexible Bayesian Synthetic Likelihood via Transformations (2020)
Supervision topics
- Surprising genomes
- Scalable Bayesian Inference using Multilevel Monte Carlo
- Efficient parameter estimation for agent-based models of tumour growth
- Topics in computational Bayesian statistics
- Predicting player performance from one format to another in cricket
- Developing predictive models, methods and analytics for complex sports data
- Equation learning for partial differential equation models of stochastic random walk models
The supervisions listed above are only a selection.