Staff Profile

I joined Newcastle as a professor of statistics in June 2023. My areas of expertise are Bayesian computational statistics and biostatistics. 

Education Background

• D.Phil in Statistics, University of Oxford, 2008
• MSc in Statistics, Beijing University, China
• BSc in Applied Mathematics, Tianjin University, China

My research in computational statistics traces back to my early career work in exact Monte Carlo simulation for graphical models and queuing modules. Currently, my focus lies on path-space rejection sampling for diffusion models, along with exploring its applications in Bayesian Fusion and Approximate Bayesian computation.

In addition, I actively engage in statistical applications and methodology development across various areas, including mixture models, survival and longitudinal models, as well as artificial intelligence.

My funded research projects:

• Pooling INference and COmbining Distributions Exactly (PINCODE), funded by EPSRC
• On intelligenCE And Networks (OCEAN), funded by ERC
• Croud Inc Ltd KTP
• Pole Star Space Applications Limited KTP
• BIAS: responsible AI for labour market equality, funded by ESRC

I welcome self-funded PhD applications.

Current PhD students: Thomas Udale, Yunfei Weng

‪Hongsheng Dai‬ - ‪Google Scholar‬

• Articles

  • Shi E, Xie J, Hu S, Sun K, Dai H, Jiang B, Kong L, Li L. Tracking Full Posterior in Online Bayesian Classification Learning: A Particle Filter Approach. Journal of Nonparametric Statistics 2024, ePub ahead of Print.
  • You N, Dai H, Wang X, Yu Q. Sequential Estimation for Mixture of Regression Models for Heterogeneous Population. Computational Statistics and Data Analysis 2024, 194, 107942.
  • Hu S, Zhang B, Dai H, Liang W. Bernoulli Factory: the 2p-coin problem. Monte Carlo Methods and Applications 2024. In Press.
  • Yang Y, Dai H, Pan J. Block-diagonal precision matrix regularization for ultra-high dimensional data. Computational Statistics and Data Analysis 2023, 179, 107630.
  • Dai H, Pollock M, Roberts GO. Bayesian fusion: Scalable unification of distributed statistical analyses. Journal of the Royal Statistical Society. Series B: Statistical Methodology 2023, 85(1), 84-107.
  • You,Na,He,Xueyi,Dai,Hongsheng,Wang,Xueqin. Ball divergence for the equality test of crossing survival curves. Statistics in medicine 2023. In Press.
  • Osuntoki IG, Harrison A, Dai H, Bao Y, Zabet NR. ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data. Bioinformatics 2022, 38(14), 3523-3531.
  • Liang W, Dai H, Restaino M. Truncation data analysis for the under-reporting probability in COVID-19 pandemic. Journal of Nonparametric Statistics 2022, 34(3), 607-627.
  • Chathoth KT, Mikheeva LA, Crevel G, Wolfe JC, Hunter I, Beckett-Doyle S, Cotterill S, Dai H, Harrison A, Zabet NR. The role of insulators and transcription in 3D chromatin organization of flies. Genome Research 2022, 32, 682-698.
  • Hu J, Liang W, Dai H, Bao Y. Efficient empirical likelihood inference for recovery rate of COVID19 under double-censoring. Journal of Statistical Planning and Inference 2022, 221, 172-187.
  • Hu S, Al-Ani JA, Hughes KD, Denier N, Konnikov A, Ding L, Xie J, Hu Y, Tarafdar M, Jiang B, Kong L, Dai H. Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation. Frontiers in Big Data 2022, 5, 805713.
  • Smith QM, Inchingolo AV, Mihailescu M-D, Dai H, Kad NM. Single-molecule imaging reveals the concerted release of myosin from regulated thin filaments. eLife 2021, 10, e69184.
  • Ma C, Dai H, Pan J. Modeling past event feedback through biomarker dynamics in the multistate event analysis for cardiovascular disease data. Annals of Applied Statistics 2021, 15(3), 1308-1328.
  • Liang W, Dai H. Empirical likelihood based on synthetic right censored data. Statistics and Probability Letters 2021, 169, 108962.