Career 2024.01- present, Assistant Professor, ShanghaiTech University 2021.12-2023.12, Associate Professor, Nankai University. 2019.12-2021.12, Assistant Professor, Nankai University. Education
2015.09-2019.09, Ph.D in Statistics, Simon Fraser University, Canada 2013.09-2015.09, M.S. in Statistics, Memorial University of Newfoundland, Canada Research My research focuses on computational statistics and statistical machine learning. Methodologically, I am particular interested in Monte Carlo methods such as Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC). My favourite application arises from genetics, evolutionary biology, public health and fishery.
Publication • S. Ge, S. Wang & L.T. Elliott (2022) Shape Modeling with Spline Partitions. Statistics and Computing (In Press). • S. Wang, S. Ge, B. Sobkowiak, L. Wang, L. Grandjean, C. Colijn & L.T. Elliott (2022) Genome-wide association with uncertainty in the genetic similarity matrix. Journal of Computational Biology (In Press). • J. Liu, Y. Yu, H. Bi, Y. Zhao, S. Wang & H. Zhang (2022) Post quantum secure fair data trading with deterability based on machine learning. Science China-Information Sciences, 65(7): 170308. • S. Wang, S. Ge, R. Doig & L. Wang (2022). Adaptive semiparametric Bayesian differential equations via sequential Monte Carlo methods. Journal of Computational and Graphical Statistics, 31(2), 600-613. • S. Wang & T. Swartz (2022). Moment matching adaptive importance sampling with skew-student proposals. Monte Carlo Methods and Applications, 28(2), 149-162. • S. Ge, S. Wang, N. Farouk & L. Wang (2022). Online Bayesian learning for mixtures of spatial spline regressions with mixed-effects. Journal of Statistical Computation and Simulation, 92(7), 1530-1566. • S. Wang, S. Ge, C. Colijn, P. Biller, L. Wang & L.T. Elliott (2021). Estimating genetic similarity matrices using phylogenies. Journal of Computational Biology, 28(6), 587-600. • X. Tang, N. Zheng, R. Rideout, S. Wang & F. Zhang (2021). Identifying recruitment regime shift using a hidden Markov stock-recruitment model. ICES Journal of Marine Science, 78(7), 2591–2602. • S. Wang & L. Wang (2021). Particle Gibbs sampling for Bayesian phylogenetic inference. Bioinformatics, 37(5), 642-649. • S. Wang, Y. Nie, J. Sutherland & L. Wang (2021). Pattern Discovery of Health Curves with an Ordered Probit Model. Statistical Methods in Medical Research, 30(2), 458-472. • S. Wang, L. Wang & T.B. Swartz (2020). Inference for misclassified multinomial data with covariates. The Canadian Journal of Statistics, 48(4), 655-669. • L. Wang, S. Wang & A. Bouchard-Côté (2020). An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. Systematic Biology, 69(1), 155-183. • S. Ge, S. Wang, Y.W. Teh, L. Wang & L.T. Elliott (2019). Random Tessellation Forests. Advances in Neural Information Processing Systems (NeurIPS, 1428/6743). • N. Zheng, S. Wang & N. G. Cadigan (2019). Local sensitivity equations for maximum sustainable yield reference points. Theoretical Population Biology, 130, 143-159. • J. Dong, S. Wang, L. Wang, J. Gill & J. Cao (2019). Joint Modelling for Organ Transplantation Outcomes for Patients with Diabetes and the End-Stage Renal Disease. Statistical Methods in Medical Research, 28(9), 2724-2737. • J. Liu, Y. Yu, B. Yang, J. Jia, S. Wang & H. Wang (2018). Structural Key Recovery of Simple Matrix Encryption Scheme Family. The Computer Journal, 61(12), 1880-1896. • S. Wang, N. G. Cadigan, & H. P. Benoit (2017). Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements. Journal of Applied Statistics, 44(6), 1013-1030. • N. G. Cadigan & S. Wang (2016). Local sensitivity of per recruit fishing mortality reference points. Journal of Biological Dynamics, 10(1), 525-545. |
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