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

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.

I organised the Adelaide Data Privacy Workshop, held in November 2025. In Paris, I organised the Mostly Monte Carlo Seminar Series. In 2023, I was chair of the SMC Down Under organising committee.

Current projects

• A game-theoretic framework for statistical data privacy “Persuasive Privacy” arXiv slides
• Knots and variance ordering of sequential Monte Carlo algorithms arXiv slides
• Variance reduction for simulation–based inference

See my full list of arXiv preprints for more.

Selected journal articles

• Bayesian score calibration for approximate models with D Warne, D Nott, and C Drovandi (2026). Journal of Machine Learning Research.
• Does Anyone Suffer From Teenage Motherhood? Mental Health Effects of Teen Motherhood in Great Britain Are Small and Homogeneous with M O’Flaherty and S Kalucza (2023). Demography.
• Accelerating sequential Monte Carlo with surrogate likelihoods with A Lee and C Drovandi (2021). Statistics and Computing.
• Polling bias and undecided voter allocations: US Presidential elections, 2004 - 2016 with T Ballard and B Baffour (2019). Journal of the Royal Statistical Society (Series A).

See Google Scholar for more.