Research

My core research focuses on physics-informed machine learning and decision-making under uncertainty, often inspired from real-world applications in science and engineering. With physics-informed machine learning, I focus on Gaussian processes and investigate how one could inject physical knowledge into them while also computationally efficient. With decision-making under uncertainty, I focus mostly on sequential decision-making – such as active learning and Bayesian optimization – with practical constraints that requires non-trivial adjustments to the adaptive rules. The application domains I am interested in include probabilistic numerics, ocean engineering, and digital twins.

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]