个人简介
本科就读于四川大学数学系，2001年至2005年作为四川大学和香港浸会大学联合培养博士生，师从方开泰教授攻读数据挖掘方向博士学位（理学博士，2005）。曾在瑞典农业大学生物统计和工程学院，香港理工大学商学院金融专业担任访问学者。贺平博士从事包括各种数据挖掘方法，化学图谱数据分析，医学健康数据分析，统计模拟等方面的研究，在国际期刊上发表研究论文20余篇， 参与编写统计学教材《随机模拟的方法和应用》。曾主持和参与多个国家和广东省基金项目，以及广东地区横向项目。贺平博士于2015年至2020 年曾担任北师港浸大统计专业系主任。
近期论文
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J.J. Wei, P. He, T.T. Jun, Estimating the Reciprocal of a Binomial Proportion, International Statistical Review (2023), doi: 10.1111/insr.12539
J.J. Wei, C. Zhu, Z.M. Zhang, P. He (2022), Two-stage iteratively reweighted smoothing splines for baseline correction, Chemometrics and Intelligent Laboratory Systems, 227. doi.org/10.1016/j.chemolab.2022.104606
Y.N. Li, K.T. Fang, P. He, H. Peng (2022) Representative points from a mixture of two normal distributions. Mathematics 10, 3952. https://doi.org/10.3390/math10213952
J.J. Liang, P. He, J. Yang, (2022) Testing Multivariate Normality Based on t-Representative Points. Axioms, 11, 587. https://doi.org/10.3390/axioms11110587
D.N. Hui, Y.Y. Sun, S.X. Xu, J.J. Liu, P. He, Y.H. Deng, H.X., Huang, X.S. Zhou, R.S. Li (2022), Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning, International Urology and Nephrology, https://doi.org/10.1007/s11255-022-03322-1
L.H. Xu, K.T. Fang, P. He (2022), Properties and generation of representative points of the exponential distribution, Statistical Papers, 63(6), doi: 10.1007/s00362-021-01236-1.
Maninder Meenu, Yaqian Zhang, Uma Kamboj, Shifeng Zhao, Lixia Cao, Ping He and Baojun Xu (2022), Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics, Foods, 11(1), 43. doi: 10.3390/foods11010043
J. Yang, P. He, K.T. Fang (2021), Three kinds of discrete approximations of statistical multivariate distributions and their applications, Journal of Multivariate Analysis, doi:10.1016/j.jmva.2021.104829
J. Liu, Y.Y. Sun, J. Ma, J.C. Tu, Y.H. Deng, P. He, R.S. Li, etc, (2021). Analysis of main risk factors causing stroke in Shanxi Province based on machine learning models. Informatics in Medicine Unlocked, doi: 10.1016/j.imu.2021.100712
K.T. Fang, P. He, Jun Yang (2020), Representative points of statistical distributions and their applications (in Chinese), Scientia Sinica Mathematica, 50 (9), 1-20, doi: 10.1360/SSM-2019-0251.
P. He, X.L. Peng, Q.S. Xu (2020), From "Clothing Standard" to "Chemometrics": Review of Prof. Kai-Tai Fang's Contributions in Data Mining. Festschrift in Honour of Professor Kai-Tai Fang. Springer-Verlag.
P. He, D. K. J. Lin, M. Q. Liu, et al. (2020), Theory and application of uniform designs (in Chinese). Scientia Sinica Mathematica, 2020, 50: 561-570, doi: 10.1360/SSM-2020-0065.
A. M. Elsawah, Fang, K.T., P. He (2019), Sharp lower bounds of various uniformity criteria for constructing uniform designs, Statistical Papers, https://doi.org/10.1007/s00362-019-01143-6
J.Y., Zhang, P. He, Kang T. Tsang and Y.H. Deng (2019), “Modeling of information diffusion in Sina Weibo based on Random Forest Classifier and SIR model”, In. Proc. Of International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019), July 2019, Kunming, China.
A. M. Elsawah, K.T. Fang, P. He and H. Qin (2017), Optimum addition of information to computer experiments in view of uniformity and orthogonality, Bull. Malays. Math. Sci. Soc.
B.Z. Ren, Y.H. Deng, P. He and Kang T. Tsang (2017), The Comparison of Four Methods in Finding Influential Spreader in Social Network, In. Proc. of International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2017) , July 2017, Guilin, China.
J.J. Jiang, P. He, K.T. Fang (2015), An Interesting Property of The Arcsine Distribution and Its Applications, Statistics and Probability Letters, 105, 88-95.
X.P. Cheng, H. Cai, P. He, R.T. Tian (2013), Combination of effective machine learning techniques and Chemometric analysis for evaluation of Bupleuri Radix through high-performance thin-layer chromatography, Anal. Methods, 5, 325-330.
P. He, Kai-Tai Fang, Yi-Zeng Liang and Bo-Yan Li (2005), A Generalized Boosting Algorithm and Its Application to Two-Class Chemical Classification Problem, Analytical Chimica Acta. 543, 181-191.
P. He, C.J. Xu, Y.Z. Liang and K.T. Fang (2004), Improving the Classification Accuracy in Chemistry Via Boosting Technique, Chemometrics and Intelligent Laboratory Systems. 70, 39-46.
P. He, K.T. Fang, Y.Z. Liang and C.J. Xu (2003), The Decision Tree Combined with SIR and Its Applications to Classification of Mass Spectra, Journal of Data Science, 1, 425-445.
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