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Ryan Chan

I graduated from the University of Leeds in 2018 with an MMath Mathematics degree and started my PhD in September 2018, under the supervision of Murray Pollock, Gareth Roberts and Petros Dellaportas. My research focuses on Monte Carlo Fusion which aims to tackle the problem of unifying distributed analyses and inferences from multiple sources on shared parameters, into a single coherent inference. This problem may arise in several settings such as expert elicitation, multi-view learning and differential privacy. A prominent example of this challenge appears in the context of ‘big data’, where for computational feasibility, typical MCMC cannot be conducted on a single machine.


Chan, R., Johansen, A.M., Pollock, M., Roberts, G.O. 2021. Divide-and-Conquer Monte Carlo Fusion. Submitted.
Chan, R., Dai, H., Pollock, M., Roberts, G.O. 2022. A simple fusion algorithm for unifying distributions. In preparation.
Chan, R. and Dai, H. 2020. Discussion of ``Quasi-Stationary Monte Carlo and the ScaLE Algorithm" by Pollock, Fearnhead, Johansen and Roberts. JRSS B.

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