Professor Hongsheng Dai
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Email
hdaia@essex.ac.uk -
Telephone
+44 (0) 1206 873304
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Location
STEM 5.5, Colchester Campus
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Academic support hours
Open door policy
Profile
Biography
Hongsheng Dai undertook his undergraduate study in Applied Mathematics at Tianjin University from 1996 to 2000 and then he went on to do his MSc in Statistics (with Shuyuan He) at Beijing University. After he completed his MSc in 2003 he came to the UK and studied for his D.Phil. (with Peter Clifford) at University of Oxford. His main research field at Oxford was Bayesian computational statistics, in particular, exact Monte Carlo simulation. Before he fulfilled all requirements of his D.Phil. in Statistics at Oxford in December 2007 (D.Phil. formally awarded in July 2008), he had already started his first academic position, as a lecturer in statistics, at Lancaster University from Sep 2006. Then he moved to Brighton University for his first permanent position in 2009 and joined University of Essex in January 2013. He is now interested in various research areas in statistical methodology and statistical applications, including Bayesian computational statistics, artificial intelligence, mixture models, graphical models, diffusion models, queuing models, non-parametric statistics, survival analysis and longitudinal analysis. PhD application Please contact me, if you have an appropriate background in statistics, mathematics, or computer science and are interested in statistics, especially in the areas of Bayesian statistics, Monte Carlo simulation, graphical models, mixture models, diffusion models and bio-statistics.
Qualifications
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BS in applied mathematics, Tianjin University (2000)
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MS in statistics, Beijing University (2003)
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D.Phil. in statistics, Oxford University (2007)
Appointments
University of Essex
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Head of Department, Mathematical Sciences, University of Essex (1/8/2021 - present)
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Professor in Statistics, Mathematical Sciences, University of Essex (1/10/2022 - present)
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Reader in Statistics, Mathematical Sciences, University of Essex (1/10/2019 - 30/9/2022)
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Director of Research, Mathematical Sciences, University of Essex (1/1/2020 - 31/7/2021)
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Postgraduate Director, Mathematical Sciences, University of essex (1/8/2013 - 31/12/2018)
Other academic
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Professor in Statistics, Mathematical Sciences, University of Essex (1/10/2022 - present)
Research and professional activities
Research interests
Bayesian computational statistics
Perfect Monte Carlo sampling
Mixture models
Graphical models
Diffusion models
Queuing models
Nonparametric statistics
Survival analysis
Longitudinal analysis
Distributed Deep Learning
Teaching and supervision
Current teaching responsibilities
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Mathematics Careers and Employability (MA199)
Previous supervision
Degree subject: Statistics
Degree type: Doctor of Philosophy
Awarded date: 20/3/2023
Degree subject: Operational Research
Degree type: Doctor of Philosophy
Awarded date: 17/10/2022
Degree subject: Statistics
Degree type: Doctor of Philosophy
Awarded date: 23/6/2022
Degree subject: Statistics
Degree type: Doctor of Philosophy
Awarded date: 14/4/2022
Degree subject: Statistics
Degree type: Doctor of Philosophy
Awarded date: 8/3/2022
Degree subject: Bio-Statistics
Degree type: Doctor of Philosophy
Awarded date: 26/1/2022
Degree subject: Data Science
Degree type: Doctor of Philosophy
Awarded date: 10/12/2021
Degree subject: Bio-Statistics
Degree type: Doctor of Philosophy
Awarded date: 6/3/2017
Degree subject: Statistics
Degree type: Doctor of Philosophy
Awarded date: 24/5/2016
Publications
Journal articles (37)
Yang, Y., Dai, H. and Pan, J., (2023). Block-diagonal precision matrix regularization for ultrahigh dimensional data. Computational Statistics and Data Analysis. 179, 107630-107630
Dai, H., Pollock, M. and Roberts, G., (2023). Bayesian Fusion: Scalable unification of distributed statistical analyses. Journal of the Royal Statistical Society Series B: Statistical Methodology. 85 (1), 84-107
Liang, W., Dai, H. and Restaino, M., (2022). Truncation data analysis for the under-reporting probability in COVID-19 pandemic. Journal of Nonparametric Statistics. 34 (3), 607-627
Hu, S., Alshehabi Al-Ani, J., Hughes, KD., Denier, N., Konnikov, A., Ding, L., Xie, J., Hu, Y., Tarafdar, M., Jiang, B., Kong, L. and Dai, H., (2022). Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation. Frontiers in Big Data. 5, 805713-
Chathoth, K., Mikheeva, L., Crevel, G., Wolfe, J., Hunter, I., Beckett-Doyle, S., Cotterill, S., Dai, H., Harrison, A. and Zabet, N., (2022). The role of Insulators and transcription in 3D chromatin organisation of flies. Genome Research. 32 (4), 682-698
Liang, W., Hu, J., Dai, H. and Bao, Y., (2022). Efficient Empirical Likelihood Inference for recovery rate of COVID-19 under Double-Censoring. Journal of Statistical Planning and Inference. 221, 172-187
Osuntoki, IG., Harrison, A., Dai, H., Bao, Y. and Zabet, NR., (2022). ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data.. Bioinformatics. 38 (14), btac387-btac387
Yang, X., Chitsuphaphan, T., Dai, H. and Meng, F., (2022). EVB-Supportive Energy Management for Residential Systems with Renewable Energy Supply. World Electric Vehicle Journal. 13 (7), 122-122
Liang, W. and Dai, H., (2021). Empirical Likelihood Based on Synthetic Right Censored Data. Statistics and Probability Letters. 169, 108962-108962
Smith, QM., Inchingolo, AV., Mihailescu, M-D., Dai, H. and Kad, NM., (2021). Single molecule imaging reveals the concerted release of myosin from regulated thin filaments. eLife. 2021 (10), e69184-
Ma, C., Dai, H. and Pan, J., (2021). Modeling past event feedback through biomarker dynamics in the multi-state event analysis for cardiovascular disease data. Annals of Applied Statistics. 15 (3), 1308-1328
Zhang, Q-Z., Dai, H., Liu, M-Q. and Wang, Y., (2019). A method for augmenting supersaturated designs. Journal of Statistical Planning and Inference. 199, 207-218
Dai, H., Pollock, M. and Roberts, G., (2019). Monte Carlo Fusion. Journal of Applied Probability. 56 (1), 174-191
Liang, W., Dai, H. and He, S., (2019). Mean Empirical Likelihood. Computational Statistics and Data Analysis. 138, 155-169
Aldahmani, S., Dai, H. and Zhang, Q-Z., (2019). Hybrid Graphical Least Square Estimation and its application in Portfolio Selection. Statistics and Its Interface. 12 (4), 631-645
Dai, H., Wang, H., Restaino, M. and Bao, Y., (2018). Linear transformation models for censored data under truncation. Journal of Statistical Planning and Inference. 193, 42-54
Dai, H. and Pan, J., (2018). Joint modelling of survival and longitudinal data with informative observation times. Scandinavian Journal of Statistics: theory and applications. 45 (3), 571-589
Sampid, M., Hasim, HM. and Dai, H., (2018). Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model. PLoS ONE. 13 (6), e0198753-e0198753
Dai, H., (2017). A new rejection sampling method without using hat function. Bernoulli. 23 (4A), 2434-2465
Alhaji, BB., Dai, H., Hayashi, Y., Vinciotti, V., Harrison, A. and Lausen, B., (2016). Bayesian analysis for mixtures of discrete distributions with a non-parametric component. Journal of Applied Statistics. 43 (8), 1369-1385
Zhang, Q., Dai, H. and Fu, B., (2016). A proportional hazards model for time-to-event data with epidemiological bias. Journal of Multivariate Analysis. 152, 224-236
Dai, H., Restaino, M. and Wang, H., (2016). A class of nonparametric bivariate survival function estimators for randomly censored and truncated data. Journal of Nonparametric Statistics. 28 (4), 736-751
Aldahmani, S. and Dai, H., (2015). Unbiased Estimation for Linear Regression When n < v. International Journal of Statistics and Probability. 4 (3)
Dai, H., (2015). Exact Simulation for Fork-Join Networks with Heterogeneous Service. International Journal of Statistics and Probability. 4 (1), 19-32
Pan, J., Bao, Y., Dai, H. and Fang, H-B., (2014). Joint longitudinal and survival-cure models in tumour xenograft experiments. Statistics in Medicine. 33 (18), 3229-3240
He, S., Park, JH., Shen, H., Wu, Z. and Dai, H., (2014). Stochastic Systems: Modeling, Optimization, and Applications. Mathematical Problems in Engineering. 2014, 1-3
He, S., Park, JH., Shen, H., Wu, Z. and Dai, H., (2014). Editorial: Stochastic Systems: Modeling, Optimization, and Applications. Mathematical problems in engineering. 2014, 1-3
Dai, H., (2014). Exact Simulation for Diffusion Bridges: An Adaptive Approach. Journal of Applied Probability. 51 (2), 346-358
Dai, H., Pan, J. and Bao, Y., (2013). Modelling Survival Events with Longitudinal Covariates Measured with Error. Communications in Statistics - Theory and Methods. 42 (21), 3819-3837
Wang, H., Dai, H. and Fu, B., (2013). Accelerated failure time models for censored survival data under referral bias. Biostatistics. 14 (2), 313-326
Bao, Y., Dai, H., Wang, T. and Chuang, S-K., (2013). A joint modelling approach for clustered recurrent events and death events. Journal of Applied Statistics. 40 (1), 123-140
Dai, H., Bao, Y. and Bao, M., (2013). Maximum likelihood estimate for the dispersion parameter of the negative binomial distribution. Statistics & Probability Letters. 83 (1), 21-27
Dai, H. and Fu, B., (2012). A polar coordinate transformation for estimating bivariate survival functions with randomly censored and truncated data. Journal of Statistical Planning and Inference. 142 (1), 248-262
Dai, H., (2011). Exact Monte Carlo simulation for fork-join networks. Advances in Applied Probability. 43 (2), 484-503
Samuels, TL., Willers, JW., Uncles, DR., Monteiro, R., Halloran, C. and Dai, H., (2011). In vitro suppression of drug-induced methaemoglobin formation by Intralipid� in whole human blood: observations relevant to the ?lipid sink theory?. Anaesthesia. 67 (1), 23-32
Dai, H. and Bao, Y., (2009). An inverse probability weighted estimator for the bivariate distribution function under right censoring. Statistics & Probability Letters. 79 (16), 1789-1797
Dai, H., (2008). Perfect sampling methods for random forests. Advances in Applied Probability. 40 (3), 897-917
Books (1)
Dai, H. and Wang, H., (2016). Analysis for Time-to-Event Data under Censoring and Truncation. 9780128054802
Book chapters (2)
Dai, H., A review on the exact Monte Carlo simulation. In: Bayesian Inference [Working Title]. Editors: Tang, N., . IntechOpen
Liang, W. and Dai, H., (2023). Bayesian inference. In: Quantum Chemistry in the Age of Machine Learning. Elsevier. 233- 250. 9780323886048
Conferences (4)
Ding, L., Yu, D., Xie, J., Guo, W., Hu, S., Liu, M., Kong, L., Dai, H., Bao, Y. and Jiang, B., (2022). Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
Chitsuphaphan, T., Yang, X. and Dai, H., (2020). Stochastic Programming for Residential Energy Management with Electric Vehicle under Photovoltaic Power Generation Uncertainty
Aldahmani, S., Dai, H., Zhang, Q-Z. and Restaino, M., (2020). Portfolio Optimisation via Graphical Least Squares Estimation
Alhaji, BB., Dai, H., Hayashi, Y., Vinciotti, V., Harrison, A. and Lausen, B., (2016). Analysis of ChIP-seq Data Via Bayesian Finite Mixture Models with a Non-parametric Component
Reports and Papers (2)
Osuntoki, IG., Harrison, A., Dai, H., Bao, Y. and Zabet, NR., (2021). ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data
Dai, H., Pollock, M. and Roberts, G., (2020). Bayesian Fusion: Scalable unification of distributed statistical analyses
Grants and funding
2022
Pooling INference and COmbining Distributions Exactly: A Bayesian Approach (PINCODE)
Engineering and Physical Sciences Research Council
2020
Using smart technology to improve oral health for those with early stage dementia
University of Essex
BIAS: Responsible AI for Gender and Ethnic Labour Market Equality
Economic and Social Research Council
2018
The project will improve efficiencies of the system, customer demand and control the price in real time.
Ocado Technology
2017
Statistical Analysis of Electricity Smart Meter Installation Failures
Stonehaven Technology Limited
Contact
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