个人简介
王洪，男，博士，副教授，博士生导师。国家认证高级程序员、系统分析师(2004年获湖南省人事厅高级专业技术资格)。2000年毕业于中南大学英语专业，获文学学士学位；2008毕业于湖南大学计算机应用技术专业，获工学硕士学位；2015年毕业于中南大学统计学专业，获理学博士学位；2017年-2018年，加州大学洛杉矶分校（UCLA）生物统计专业博士后。主要从事机器学习和生物统计等方面的研究工作。在Statistics in Medicine、Artificial Intelligence in Medicine 、Knowledge-Based Systems、Reliability Engineering and System Safety等统计和机器学习期刊发表SCI论文20余篇，获软件著作权1项。主持和参加国家社科基金、国家自科基金项目4项，主持全国统计科研项目重点项目、教育部人文社科基金等省部级目4项，主持企业合作横向课题多项。 是Statistics in Medicine、Artificial Intelligence in Medicine、Computers in Biology and Medicine、Statistica Sinica等20余种SCI杂志的审稿人。
欢迎来自统计学、经济学、计算机和数学等相关专业背景以及对科研有浓厚兴趣和热情的考生报考。研究小组将对表现优异的研究生，每月提供学习和研究期间额外生活补助。
关于硕士招生(专硕最多1人，学硕、专硕共计3人)：
(1) 本科学校背景仅做参考，主要看个人综合能力，有很强编程水平(R, Python, C++)。
(2) 学硕专硕，同等培养。自2022年起，本组硕士生必须录用或发表SCI论文1篇才能达到毕业要求，请慎重填报。
(3) 联系时请将自己主要优势、竞赛作品，论文，证书，成绩及排名情况等一并发送。
(4) 以下情况，请勿联系：无省部级级及以上建模竞赛获奖；论文作品仅挂名，无法阐述详细内容；英语未通过CET6者。
代表性论文(近5年，#表示共同作者，*表示通讯作者)：
Xuewei Cheng, Hong Wang*, A Generic Model-Free Feature Screening Procedure for Ultra-high Dimensional Data with Categorical Response, Computer Methods and Programs in Biomedicine, 2022. Accepted (SCI, JCR1区, IF=7.027)
Hanpu Zhou, Hong Wang*, Sizheng Wang and Yi Zou. SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis, R Journal, 2022. Accepted (SCI, JCR2区, IF=1.673)
Xuewei Cheng,Sizheng Wang, Yi Zou, Badamasi Abba,Hong Wang*, Deep survival forests with feature screening, Biomedical Signal Processing and Control, 2023, 79(2): 104195 (SCI, JCR2区, IF=5.076)
Hong Wang, Zhenyuan Shen, Zhelun Tan, Zhuan Zhang, Gang Li*, Fast Lasso-type Safe Screening for Fine-Gray Competing Risks Model with Ultrahigh Dimensional Covariates, Statistics in Medicine , 2022, 41(24): 4941-4960 (SCI, JCR1区, IF=2.497)
Bing Tian, Zili Liu, Hong Wang*, Non-marginal Feature Screening for Varying Coefficient Competing risks model, Statistics and Probability Letters, 2022, 190: 109648, (SCI, JCR4区，IF=0.718)
Xuewei Cheng, Sizheng Wang, Hong Wang* and Shu Kay Ng, Deep survival forests for extremely high censored data, Applied Intelligence, 2022, https://doi.org/10.1007/s10489-022-03846-0 (SCI, JCR2区, IF=5.086)
Hong Wang#,Badamasi Abba#, Jianxin Pan*, Classical and Bayesian Estimations of Improved Weibull-Weibull Distribution for Complete and Censored Failure Times Data, Applied Stochastic Models in Business and Industry, 2022, https://doi.org/10.1002/asmb.2698 (SCI，JCR3区，IF=1.338）
Badamasi Abba, Hong Wang*, Hassan S. Bakouch, A Reliability and Survival Model for One and Two Failure Modes System with Applications to Complete and Censored Datasets，Reliability Engineering and System Safety，2022, 223: 108460（SCI，JCR1区，IF=6.188）
Zhou, Lifeng, Wang, Hong*. A Combined Feature Screening Approach of Random Forest and Filter-based Methods for Ultra-high Dimensional Data, Current Bioinformatics, 2022, 17(4): 394 -407 (SCI, JCR1区，IF=4.85)
Zhuan Zhang#, Zhenyuan Shen#, Hong Wang*, Shu Kay Ng. A Fast Adaptive Lasso for the Cox Regression via Safe Screening Rules, Journal of Statistical Computation and Simulation, 2021, 91(14): 3005-3027 (SCI, JCR3区，IF=1.424)
Hong Wang, Ning Li, Shanpeng Li, and Gang Li*, JMcmprsk: An R Package for Joint Modelling of Longitudinal and Survival Data with Competing Risks, R Journal, 2021, 13(1): 53-68 (SCI, JCR1区，IF=3.984)
NF Zhao, QX Xu, ML Tang and H Wang*,"Variable screening for near infrared (NIR) spectroscopy data based on ridge partial least squares regression", Combinatorial Chemistry & High Throughput Screening, 2020, 23(8):740-756. (SCI, JCR3区, IF=1.195)
NF Zhao, QX Xu, ML Tang, BY Jiang, ZQ Chen, and H Wang*,"High Dimensional Variable Screening under Multicollinearity",Stat, 2020, 9, e272. (SCI, JCR3区, IF=0.766)
Wang, Hong, and Gang Li*."Extreme Learning Machine Cox Model for High Dimensional Survival Analysis", Statistics in Medicine, 38.12, (2019): 2139-2156, (SCI, JCR1区, IF=1.932)
Wang, Hong, and Lifeng Zhou*. "SurvELM: an R package for high dimensional survival analysis with extreme learning machine", Knowledge-Based Systems, 160 (2018):28-33. （SCI,JCR1区, IF=4.396)
Wang, Hong, Jianxin Wang*, and Lifeng Zhou. "A survival ensemble of extreme learning machine." Applied Intelligence 48.7 (2018): 1846-1858.（SCI,JCR2区, IF=1.983)
Zhou, Lifeng, Hong Wang*, and Qingsong Xu. "Survival forest with partial least squares for high dimensional censored data." Chemometrics and Intelligent Laboratory Systems 175 (2018), 12-21.（SCI,JCR1区, IF=2.701)
Wang, Hong, Xiaolin Chen, and Gang Li*. "Survival Forests with R-Squared Splitting Rules." Journal of Computational Biology 25.4 (2018): 388-395.（SCI,JCR2区, IF=1.191)
Wang, Hong, and Lifeng Zhou*. "Random survival forest with space extensions for censored data." Artificial Intelligence in Medicine 79 (2017): 52-61.（SCI,JCR1区, IF=2.879)