个人简介
王洪，男，博士，副教授，博士生导师。文学学士（英语专业，中南大学）、工学硕士（计算机应用技术，湖南大学）、理学博士（统计学，中南大学）、计算机科学与技术博士后（中南大学）、生物统计专业博士后（美国加州大学洛杉矶分校UCLA）。国家认证高级程序员、系统分析师。主要从事生物医学统计、机器学习和大数据挖掘等方面的研究工作。第一和通讯作者发表SCI论文30余篇，出版教材1部（机械工业出版社）。主持国家社科基金一般项目、全国统计科研项目重点项目、教育部社科基金、湖南省社科基金、湖南省自科基金等纵向项目多项，主持企业合作横向课题多项。是中国现场统计研究会大数据统计分会常务理事，是澳大利亚研究理事会（ARC）研究项目通讯评审专家，30余种SCI杂志的审稿人。
近年来发表论文(#表示共同作者，*表示通讯作者)：
1. Wang H, Ling Hong, A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring Via Safe Screening, Big Data, 2024. DOI: 10.1089/big.2023.0033.
2. Zili Liu,Wang H, Chunjie Wang and Xinyuan Song*, Simultaneous Variable Selection and Estimation of Survival Model with Informative Censoring, Statistica Sinica, 2024, DOI: 10.5705/ss.202023.0227（和香港中文大学宋心远教授合作，博士生是第一作者）
3. Cheng X, Li G, Wang H*. The concordance filter: an adaptive model-free feature screening procedure[J]. Computational Statistics, 2024, 39(5): 2413-2436. （和UCLA大学Gang Li教授合作，博士生是第一作者）
4. Liu Z, Wang H*. Simultaneous variable selection and estimation for survival data via the Gaussian seamless‐L 0 penalty[J]. Statistics in Medicine, 2024, 43(8): 1509-1526. （博士生是第一作者）
5. Abba B, Wang H*, Muhammad M, et al. A robust bathtub-shaped failure time model for a two-component system with applications to complete and censored reliability data[J]. Quality Technology & Quantitative Management, 2024, 21(3): 309-339.（博士生是第一作者）
6. Abba B, Wang H*. A new failure times model for one and two failure modes system: A Bayesian study with Hamiltonian Monte Carlo simulation[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2024, 238(2): 304-323.（博士生是第一作者）
7. Cheng X, Wang H*. A generic model-free feature screening procedure for ultra-high dimensional data with categorical response[J]. Computer Methods and Programs in Biomedicine, 2023, 229: 107269.（博士生是第一作者）
8. Cheng X, Wang S, Wang H*, Shu Kay Ng . Deep survival forests for extremely high censored data[J]. Applied Intelligence, 2023, 53(6): 7041-7055.（和澳大利亚Shu Kay Ng教授合作，博士生是第一作者）
9. Cheng X, Wang S, Zou Y, Wang H*. Deep survival forests with feature screening[J]. Biomedical Signal Processing and Control, 2023, 79: 104195.（博士生是第一作者）
10. Zhou H, Wang H*, Wang S, et al. SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis[J]. R Journal, 2022, 14(4).（硕士生是第一作者）
11. Tian B, Liu Z, Wang H*. Non-marginal feature screening for varying coefficient competing risks model[J]. Statistics & Probability Letters, 2022, 190: 109648.（硕士生是第一作者）
12. Wang H, Shen Z, Tan Z, Li G*. Fast Lasso‐type safe screening for Fine‐Gray competing risks model with ultrahigh dimensional covariates[J]. Statistics in Medicine, 2022, 41(24): 4941-4960.（和UCLA大学Gang Li教授合作）
13. Abba B, Wang H*, Bakouch H S. A reliability and survival model for one and two failure modes system with applications to complete and censored datasets[J]. Reliability Engineering & System Safety, 2022, 223: 108460.（博士生是第一作者）
14. Zhou L, Wang H*. A Combined Feature Screening Approach of Random Forest and Filterbased Methods for Ultra-high Dimensional Data[J]. Current Bioinformatics, 2022, 17(4): 344-357.
15. Wang H, Abba B, Pan J*. Classical and Bayesian estimations of improved Weibull–Weibull distribution for complete and censored failure times data[J]. Applied Stochastic Models in Business and Industry, 2022, 38(6): 997-1018. （和曼彻斯特大学、北师港浸大UIC潘建新教授合作）
16. Zhang Z#, Shen Z#, Wang H*, Shu Kay Ng. A fast adaptive Lasso for the cox regression via safe screening rules[J]. Journal of Statistical Computation and Simulation, 2021, 91(14): 3005-3027. （和澳大利亚Shu Kay Ng教授合作，硕士生是第一作者）
17. Wang H, Li N, Li S, Li G*. JMcmprsk: An R Package for Joint Modelling of Longitudinal and Survival Data with Competing Risks[J]. R Journal, 2021, 13(1): 53. （和UCLA大学Gang Li教授合作）
18. Zhao N, Xu Q, Tang M, Wang H*. Variable screening for near infrared (NIR) spectroscopy data based on ridge partial least squares regression[J]. Combinatorial Chemistry & High Throughput Screening, 2020, 23(8): 740-756.
19. Zhao N, Xu Q, Tang M L,Wang H*. High‐dimensional variable screening under multicollinearity[J]. Stat, 2020, 9(1): e272.
20. Wang H, Li G*. Extreme learning machine Cox model for high‐dimensional survival analysis[J]. Statistics in medicine, 2019, 38(12): 2139-2156. （和UCLA大学Gang Li教授合作）
21. Wang H, Zhou L*. SurvELM: an R package for high dimensional survival analysis with extreme learning machine[J]. Knowledge-Based Systems, 2018, 160: 28-33.
22. Zhou L, Wang H*, Xu Q. Survival forest with partial least squares for high dimensional censored data[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 179: 12-21.
23. Wang H, Wang J*, Zhou L. A survival ensemble of extreme learning machine[J]. Applied Intelligence, 2018, 48: 1846-1858.（和中南大学王建新教授合作）
24. Wang H, Chen X, Li G*. Survival forests with R-squared splitting rules[J]. Journal of Computational Biology, 2018, 25(4): 388-395. （和UCLA大学Gang Li教授合作）
25.Wang H, Zhou L*. Random survival forest with space extensions for censored data[J]. Artificial intelligence in Medicine, 2017, 79: 52-61.
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