Fangyuan Yu is a Ph.D. student in the Statistics program at KAUST, his supervisor is Professor Ajay Jasra.

Education and Early Career

Fangyuan holds a master's degree in statistics from the National University of Singapore (NUS). He also holds a master's degree and a bachelor's degree in mathematics and applied mathematics at Shandong University, China.

Before joining KAUST, Fangyuan worked as a research assistant in the Department of Statistics and Applied Probability, National University of Singapore from August 2018 to July 2019. His principal investigator was Professor Ajay Jasra.

Research Interest

His main areas of research are computational probability, Bayesian Inference, Monte Carlo methodology, Stochastic filtering, MCMC methodology, and Recursive Neural Network.


(2021) Jasra, A., Law KJH & Yu F. Unbiased Filtering of a Class of Partially Observed Diffusions.  Advances in Applied Probability (to appear)

(2021) Jasra, A., Law KJH & Yu F. Randomized MLMC. Smoky Mountains Computational Sciences & Engineering Conference. 

(2020) Jasra, A. & Yu F. CLTs for coupled particle filters.  Advances in Applied Probability, 52, 942-1001.

(2020) Jasra, A., Yu, F. & Heng, J. MLPF for filtering in continuous time. Stat. Comp. 30, 1381-1402.


(2021) Chada, N., Jasra, A., & Yu, F. Unbiased hessians for diffusions.

(2020) Chada, N.K, Jasra, A. & Yu, F.  Multilevel ensemble Kalman-Bucy filters.