Research

My core research focuses on decision-making under uncertainty – such as active learning and Bayesian optimization – where adaptive rules are designed to tackle real-world decision problems. I am especially interested in scenarios where we could leverage physical laws while accommodating practical limitations (e.g. restricted domains, computational budgets, unconventional data sources). These conditions shape the nature of decision-making, and often lead to developments of novel physics-inspired machine learning models and techniques. The applications 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)

  • 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. [arxiv] [code] (submitted)

Publications and Workshop Papers

  • 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]