I am a lecturer at Adelaide University. Previously I worked at Université Paris Dauphine–PSL, and before that at the Queensland University of Technology in the Centre for Data Science and School of Mathematical Sciences.

My research involves designing computationally efficient (sometimes private) algorithms that learn from data with principled uncertainty quantification.

I develop and analyse algorithms for Bayesian inference, including sequential Monte Carlo and Markov chain Monte Carlo. I work with applied scientists to develop principled and robust analysis procedures for their data. This involves designing Bayesian models to incorporate domain and expert knowledge, and developing bespoke algorithms. I also design open source software for statisticians, scientists and industry.

In Paris, I organised the Mostly Monte Carlo Seminar Series. In 2023 I was chair of the committee organising SMC Down Under.

Some current projects

• A Bayesian decision-theoretic framework for statistical data privacy
• Variance reduction in sequential Monte Carlo with Feynman–Kac knots
• Calibrating surrogates models for intractable Bayesian posteriors arXiv

Selected journal articles

• “Accelerating sequential Monte Carlo with surrogate likelihoods” (2021) Statistics and Computing https://doi.org/10.1007/s11222-021-10036-4 with A Lee, and C Drovandi.
• “Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables” (2020). Journal of Official Statistics https://doi.org/10.2478/jos-2020-0015 with B Baffour, M Spallek and M Haynes.
• “Polling bias and undecided voter allocations: US Presidential elections, 2004 - 2016” (2019). Journal of the Royal Statistical Society (Series A) https://dx.doi.org/10.1111/rssa.12414 with T Ballard and B Baffour.
• “Fitting monotone polynomials in mixed effects models” (2019). Statistics and Computing https://doi.org/10.1007/s11222-017-9797-8 with BA Turlach and K Murray.

See Google Scholar for more details.