Research

I am broadly interested in computational statistics and machine learning methodologies, and their applications in scientific disciplines. In particular, I am interested in (1) developing methodologies with strong relevance to applications, and (2) advancing our understandings of the underlying mechanisms behind existing, commonly used algorithms. Below are some of the keywords that interest me:

• Sequential Experiment Design, Bayesian Optimisation, Active Learning
• Amortized Inference, Simulation-Based Inference
• MCMC, SMC, Variational Bayes, Normalising Flows
• Gaussian Process Modelling

Currently, I am investigating how active learning, in combination with physics-informed Gaussian processes surrogate models, can help ocean engineers and oceanographers better understand ocean currents and inform their exploration strategies.

Publication

Google Scholar

Preprints

• Livingstone, S., Nüsken, N., Vasdekis, G., & Zhang, R. Y. (2024). Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. [arxiv] (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] (accepted)