Research

I am broadly interested in the decision aspects of Bayesian computational statistics and machine learning. In particular, my research focuses on developing sequential Bayesian designs – such as active learning and Bayesian optimization – for a wide range of application areas including probabilistic numerics and ocean engineering. I am also exploring what post-Bayesian ideas could offer for more robust, efficient decision-making.

Publication

Google Scholar

Preprints

  • Zhang, R. Y., Moss, H. B., Astfalck, L. C., Cripps, E. J., and Leslie, D. S. (2025). BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields. [arxiv] (submitted)

Publications and Workshop Papers

  • Iguchi, Y., Livingstone, S., Nüsken, N., Vasdekis, G., & Zhang, R. Y. (2025+). Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. IMA Journal of Numeical Analysis. [arxiv] [code] (forthcoming)

  • Zhang, R. Y., Moss, H. B., Astfalck, L. C., Cripps, E. J., and Leslie, D. S. (2024). BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories. NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty. [OpenReview] [slides] [talk]