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.

Oftentimes, a Gaussian process is the default emulator model class for systems of interest in decision-making, so much of my research is also dedicated to devising scalable and robust Gaussian process inference.

Publication

Google Scholar

Preprints

Publications and Workshop Papers

  • Zhang, R. Y., Astfalck, L. C., Cripps, E. J., and Leslie, D. S., Moss, H. B. (2026). BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields. International Conference on Machine Learning (ICML). [arxiv] [code] [talk] [poster]

  • Iguchi, Y., Livingstone, S., Nüsken, N., Vasdekis, G., & Zhang, R. Y. (2026). Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. IMA Journal of Numeical Analysis. [arxiv] [paper] [code]

  • 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]