Associate Professor Department of Statistics and Data Science

Personal Profile

Professional Experience

Associate professor, Department of Mathematics, SUSTech, Shenzhen. 07/2019-Present

Assistant professor, Department of Mathematics, SUSTech, Shenzhen. 07/2015-06/2019

Assistant professor, Department of Financial Mathematics and Financial Engineering, SUSTech, Shenzhen. 07/2013-06/2015

Associate professor, Postgraduate tutor, School of Statistics and Mathematics, Zhongnan University of Economics and Law,  Wuhan. 10/2010-06/2013

Assistant Professor, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan. 10/2010-09/2011

Postdoctoral Fellow, The Chinese University of Hong Kong Hong Kong.  09/2009-09/2010


Educational Background:

P.H.D The Chinese University of Hong Kong

Msc Yunnan University

Bsc National University of Defense Technology


Honor and Awards:

Excellent Teacher Award, SUSTech, 2018. 

Excellent Teacher Honour, Shenzhen City, 2018.

Excellent Mentor Award, SUSTech, 2016

Peacock Plan Talent Programme, Shenzhen City, 2016


Project Held

1. NSFC Award (General programme)

Grant Number: 11871263.

Project Name:Likelihood inference for high-dimensional parametric and semi-parametric models

Amount of Funding: RMB 550,000.00                   

Research period: 01/2019-12/2022

2. NSFC Award (Youth programme)

Grant Number:11101432,

Project Name:Inference of DTARCH, GARCH and FARCH models based on Weighted Composite Quantile Regression

Amount of funding: RMB 210,000.00

Research period: 01/2012-12/2014  

3.  NSFC from Guangdong Province

Grant Number: 2017A030313012

Project Name:A dynamic Bayesian statistical study on the AIDS and other major epidemic diseases control

Amount of funding: RMB 100,000.00

Research period: 06/2016-06/2019

4.  NSFC from Guangdong Province

Grant Number: 2016A030313856

Project:A Study of Model Selection and Statistical diagnosis for Count Data

Amount of funding RMB: 100,000.00             

Research period: 06/2016-06/2019

5.  Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20170307110329106

Project Name:Study on risk prediction and dynamic prevention of AIDS epidemic in Shenzhen City

Amount of funding: RMB 300,000.00         

Research period: 06/2017-06/2019

6.  Enterprise Horizontal Research Programme

Grant Number: K1628Z015

Project Name:A quantitative trading system based on depth machine learning

Amount of funding: RMB 200,000.00                                

Research period: 08/2016-08/2018


7.  NSFC (General programme)

Grant Number: 1157116

Project Name:Research on Positioning Imaging Theory and Algorithms of Electromagnetic Inverse Scattering Problems

Amount of funding: RMB 550,000.00                                                 

Research period: 01/2016-12/2019

8.  Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20140509143748226

Project Name:Research on Relevant Theory and Algorithms of Inverse Scattering Problem

Amount of funding: RMB 300,000.00                                               

Research period: 01/2015-12/2016


· Quantile Regression

· Parametric and nonparametric inference

· High-dimensional data analysis

· Statistics in Financial Econometrics

· Bayesian analysis and its application


Multivariate Statistics Analysis (2018 Spring)

Econometrics (2018 Spring)

Time Series Analysis (2018 Fall)

Advanced Statistics (2018 Fall, postgraudates)

Publications Read More

· Tan, F., Jiang. X., Guo, X. and Zhu, L. (2019). Testing heteroscedasticity for regression models based on projections. Statistica Sinica, online.

· Guo, X., Jiang. X., Zhang, S. and Zhu, L. (2019). Pairwise distance-based heteroscedasticity for regressions. Science China- Mathematics, online.

· Jiang, X., Fu, Y., Jiang, J., Li, J. (2019). Spatial Distribution of the Earthquake in Mainland China. Physica A: Staitsical Mechanics and its Application, online.

· Jiang, X., Li, Y. , Yang, A. and Zhou, R. (2018). Bayesian semiparametric quantile regression modeling for estimating earthquake fatality risk. Empirical Economics, online.

· Lin, H., Jiang, X., Liang, H. and Zhang, W. (2018). Reduced rank modelling for functional regression with functional responses. Journal of multivariate analysis,169,205-217.

· Jiang, X. and Fu, Y. (2018). Measuring the Benefits of Development Strategy of “The 21st CenturyMaritime Silk Road” via Intervention Analysis Approach: Evidence from China and Neighboring Countries in Southeast Asian. Panoeconomicus,65(5)

· Xia, T., Jiang, J. and Jiang, X. (2018). Local influence for quasi-likelhood nonlinear models with random effects. Journal of Probability and Statistics. Vol 2018. 7.

· Li, J., Jiang, J., Jiang, X. and Liu, L.(2018). Risk-adjusted Monitoring of Surgical Performance. PLOSONE, 13(8), 1-13

· Zhao, W., Jiang, X. and Liang H. (2018). A Principal Varying-Coefficient Model for Quantile Regression: Joint Variable Selection and Dimension Reduction. Computational Statistics and Data Analysis,127, 269-280. (2018,11)

· Yang, A., Jiang, X., Shu, L., Lin, J. (2018). Sparse Bayesian Kernel Multinomial Probit Regression Model for High-dimensional Data Classification. Communication in statistics-theory and methods. Online

· Tian, G., Liu, Y., Tang, M. and Jiang, X. (2018). Type I multivariate zero-truncated/adjusted distributions with applications. Journal of computational and applied mathematics,344(15), 132-153.

· Jiang X., Guo, X., Zhang, N., Wang, B. and Zhang, B.* (2018). Robust multivariate nonparametric tests for detection of two- sample location shift in clinical trials. PLOSONE,13(4), 1-20.

· Yan A., Liang H., Jiang X. and Liu P. (2018). Sparse Bayesian variable selection for classifying high-dimensional data. Statistics and its interface,11(2), 385-395.

· Tian, G., Zhang, C. and Jiang, X. (2018). Valid statistical inference methods for a case-control study with missing data. Statistical Methods in Medical Research,27(4), 1001-1023.

· Xia T., Jiang X. and Wang X. (2018). Asymptotic properties of approximate maximum quasi-likelihood estimator in quasi- likelihood nonlinear models with random effects. Communication in Statistics,47, 1-12.

· Song, X. Kang, K. Ouyang, M., Jiang, X. and Cai. J. (2018). Bayesian Analysis of Semiparametric Hidden Markov Models with Latent Variables. Structural Equation Modeling: A Multidisciplinary Journal.25(1), 1-20.

· Li J., Liang, H., Jiang, X. and Song, X. (2018). Estimation and Testing for Time-varying Quantile Single-index Models with Longitudinal Data. Computational Statistics and Data Analysis,118, 66-83.

· Feng, K. and Jiang, X. (2017). Variational approach to shape derivatives for elasto-acousticcoupled scattering fields and an application with random interfaces. Journal of Mathematical Analysis and Application,456, 686-704.

· Jiang, J., Jiang. X., Li, J. Li, Y and Yan, W. (2017). Spatial Quantile Estimation of Multivariate Threshold Time Series Models. Physical A: Statistical Mechanics and Its Application,486,772-781.

· Guo, X., Jiang, X. and Wong, W. (2017). Stochastic Dominance and Omega Ratio: Measures to Examine Market Efficiency and Anomaly. Economies, 5(38),1-16.

· Tian, X., Jiang, X., and Wang, X. (2017). Diagnostics for quasi-likelihood nonliear models. Communication in Statistics-Theory and Methods,47(16), 8836-8851.

· Jiang, X., Tian, X. and Wang, X. (2017). Asymptotic properties of maximum quasi-likelihood estimator in quasi-likelihood nonlinear models with stochastic regression. Communication in Statistics-Theory and Methods,46(13), 6229-6239. 22.

· Niu, C. and Jiang, X. (2017). Statistical inference for a novel health inequality index. Theoretical Economics Letters,7, 251-262.

· Yang, A, Jiang, X., Xiang, L and Lin J. (2017). Sparse Bayesian Variable Selection in Multinomial Probit Regression Model with Application to High-dimensional Data Classification. Communication in Statistics-Theory and Methods.46(12), 6137-6150.

· Yang, A., Jiang, X., Shu, L. and Lin J. (2017). Bayesian Variable Selection with Sparse and Correlation Priors for High-dimensional Data Analysis. Computational Statistics,32, 127-143 .

· Huang, X., TIAN, G., Zhang, C. and Jiang, X. (2017). Type I multivariate zero-inflated generalized Poisson distribution with applications. Statistics and Its Interface,10(2), 291-311.

· Yang, A., Jiang, X., Liu, P. and Lin J. (2016). Sparse bayesian multinomial probit regression model with correlation prior for High-dimensional data Classification. Statistics and probability letters,119,241-247.

· Jiang, X., Li, J., Xia, T and Wang, Y. (2016) Robust and efficient estimation with weighted composite quantile regression. Physical A: Statistical Mechanics and its Applications,457, 413-423.

· Jiang, X., Song, X. and Xiong, Z. (2016) Robust and efficient estimation of GARCH models. Journal of Testing and Evaluation,44(5), 1-23.

· Li, H., Tian, G., Jiang, X. and Tang, N. (2016). Testing hypothesis for a simple ordering in incomplete contingency tables. Computational Statistics and Data Analysis,99,25-37.

· Li, Y., Tang, N. and Jiang, X. (2016). Bayesian Approaches for Analyzing Earthquake Catastrophic Risk. Insurance: Mathematics and Economics, 68, 110-119.

· Xia, T., Jiang, X. and Wang, X. (2015). Strong consistency of the maximum quasi-likelihood estimator in quasi-likelihood nonlinear models with stochastic regression. Statistics & Probability letters,103, 37-45

· Xia, T., Wang, X. and Jiang, X. (2014). Asymptotic properties of maximum quasi-likelihood estimator in quasi likelihood nonlinear models with misspecified variance function. Statistics,48(4), 778-786.

· Song, X., Cai, J., Feng, X. and Jiang, X. (2014). Bayesian Analysis of Functional-Coefficient Autoregressive Heteroscedastic Model. Baysian Analaysis,9(2), P1-26.[PDF]

· Jiang, X., Tian, T. and Xie, D. (2014). Weighted type of quantile regression and its application. IMECS2014, II, 818-822.

· Jiang J, Jiang, X. and Song X(2014) Weighted composite quantile regression estimation of DTARCH models. The Econometrics Journal, 17(1),1-23 [PDF]

· Jiang, X., Jiang, J. and Song, X. (2012.). Oracle model selection for nonlinear models based on weighted composite nonlinear quantile regression. Statistica Sinica,22(4), 1479-1506.[PDF]

· Jiang, J. and Jiang, X. (2011). Inference for partly linear additive COX models. Statistica Sinica,21(2),901-921.[PDF]

· Jiang, X., Jiang, J. and Liu, Y. (2011). Nonparameteric regression under double-sampling designs. Journal of Systems Science and Complexity,24, 1-9.

· Xia, T., Wang, X. and Jiang, X. (2010). Asymptotic properties of the MLE in nonlinear reproductive dispersion models with stochastic regressors. Communication in Statistics,Theory and Methods,39, 2800-2810.

· Jiang, J., Marron, J.S. and Jiang, X.(2009). Robust Centroid Quantile Based Classification for High Dimension Low Sample Size Data. Journal of Statistical Planning and Inference,139(8), 2571-2580.

· Jiang, J., Zhou, H.,Jiang, X. and Peng, J. (2007). Generalized likelihood ratio tests for the structures of semiparametric additive models. The Canadian Journal of Statistics,35(3), 381-398.


· Jiang J. and Jiang X.* (2019). Nonparametric Inference for Partly Linear Additive Cox Models based on Polynomial Spline Estimation. Journal of the Americal Statistical Association, revision.[PDF]

News More

  • SUSTech Launches New Dept of Statistics and Data Science

    Earlier today, Southern University of Science and Technology (SUSTech) launched the Department of Statistics and Data Science (SDS), before hosting the Symposium of Data Science. SUSTech President Chen Shiyi, Vice President Tang Tao and Acting Vice President Zhang Dongxiao all attended the SDS launch. They were joined by academicians, fellows, experts and scholars from top […]

  • 杰出教学奖获得者蒋学军:在南科大任教是一生无悔的选择

    回想进入南方科技大学任教的5年时光,数学系助理教授蒋学军感触良多,对几件事记忆尤为深刻:一个是5年前,他被南科大办学理念吸引,放弃在武汉一所重点高校的教职,来到深圳并亲历了南科大发展之路,在今年的教师节上,获学校授予“五年服务奖”;一个就是今年获得学校的年度“杰出教学奖”,相对于近几年他在科研教学中获得的其他奖项,他认为这对一个老师来说是最好的肯定。 蒋学军2013年7月加入南科大数学系。5年来,他承担了繁重的教学任务,指导了数十位本科生毕业,积极推动本科生实习工作,对学校的金融数学和统计学教学做出很大贡献。他还不断探索教学改革,主持省级教改课题1项,校级教改项目1项,录制了时间序列分析MOOC课程,并与田国梁教授合著了一部即将出版的教材Mathematics Statistics。他非常注重课堂师生互动与教学方法创新,在课程设计、翻转课堂等方面颇有心得。 采访蒋学军 培养学习主动性和钻研能力是首要 谈及本科教育,蒋学军认为本科生学习最大的优势是接受能力快,不会有固定的思维束缚自己的想法。因此,他也在不断地因材施教,希望可以培养学生良好的学习习惯和品质。 主动学习和喜欢钻研的精神是他最看重的,也是他最想要培养学生的品质。他定期选择课题和学生进行面对面讨论,每星期的组会最多时有4次之多,对于每次的组会主题,蒋学军会提前告诉学生需要查阅的文献,具体的解决办法他会引导学生独立思考探索。 蒋学军认为,要注重培养学生扎实的基础知识和专业知识,像南科大这种高起点、高水平的研究型大学更应如此。本科生招生数量相对较少,本科生的培养上就更要高质量,有条件的院系可以考虑对本系前20%的学生进行更高要求的培养,在课程培养和专业训练上可以加大难度和广度,甚至可以在本科阶段对这些学生提前进行研究生阶段的教育。 他对本科生的学习内容期望和南科大通识教育的培养方案不谋而合,“作为学生,最重要的是打好扎实的知识基础,兴趣尽量广泛,掌握不只一门学科的专业内容”。 蒋学军建议刚入学的学生首先要学会分配和管理自己的时间,不要辜负大学相对自由的时间,对自己的行为负责。在此基础上要尽力对学习和生活有规划、有目标。他希望南科大学生在学习上能迎难而上,要有好奇心和刻苦钻研的精神。 能够吸引学生的课堂才是好课堂 对教学的思考最终都要落在实践中。对于课堂教学,蒋学军坦言自己也会抽查学生是否到课,但他认为保证教学效果的最好办法是尽可能多地吸引学生的注意力。对此,他有自己的心得和授课技巧。比如学生之间的理解力参差不齐,他的解决之道就是让听懂的同学来分享,这种反转课堂的教学方式能够有效地吸引学生们的注意力,解决一些同学在理解上的困惑,让学生之间互相帮助,使课堂效果事半功倍。为了让自己的课堂有多样的互动,使课堂更加有趣,他对备课极其认真。 蒋学军谈到,备好课是上好课的基础,尤其是在新课的准备上,有时候为了备好一堂新课,可能需要花上两三天的时间。他认为充分备课能够帮助形成具有自身独特风格的教学讲义和课件,为以后任教这门课程节省时间。 蒋学军习惯在第一次备课时就把课件、讲义、练习题、作业和答案、小测验及答案、Project、期中(末)考试卷和答案分类整理好,课后根据学生的反馈继续完善课件,并记录上课的心得,他还会出一些练习题作为后面的小测验和考试的题目,这种习惯让他积累了自己的题库,能够更有效地帮助学生巩固和复习,也为后面的试卷命题带来很多好的资料。在课堂讲授之外,他还会穿插播放平时录制好的课程视频给学生看。 有时候白天繁忙的事务性工作挤压了备课的时间,备课到深夜两点甚至天亮也曾发生过。在课堂外,经常有学生会到他办公室来问问题,不管多忙,蒋学军都会停下手头的工作耐心地讲解。 几分耕耘,几分收获。充分的备课和丰富多彩的教学形式让蒋学军的课堂倍受欢迎。 采访后的一天,我们特意去感受了蒋学军的课堂教学风采。当记者到达时,蒋学军的《时间序列分析》课程已经开始。整个教室座无虚席,讲台上黑板左侧放着ppt,他正在黑板上板书给同学们讲解课题,洪亮有力的声音充满了课堂,粉色、白色、黄色的字迹布满了黑板。课间他没有休息,很多同学拿着课堂笔记到讲台去问问题。 选择南科大是一生不后悔的事 在来南科大之前,蒋学军在武汉的一所大学从事教学科研工作,但是了解南科大后,他为南科大的独特所吸引。有过香港求学和研究的经历,南科大宽松的学术氛围和教学氛围,英语教学等方式对蒋学军产生了莫大的吸引力。他认为南科大集合了国内外众多大学的优秀品质,未来发展令人期待。 为此,他放弃了在武汉已经稳定的工作和熟悉的生活环境,来到中国改革开放的前沿、来到南科大。他的夫人给予他很大支持,也坚定地随他一起来到深圳打拼,在这里相互扶持、共同进步。 说起这段往事,蒋学军难掩对岁月的感慨和对夫人的感激。他坦言最初来到南科大和自己的想象存在差距,但在多方共同努力下,快速成长的南科大并没有辜负他的期望,他从未曾后悔过这次人生的选择。   在我们即将结束采访时,一名学生拿着课本到办公室讨论问题,我们恰好拍摄了这个过程。蒋学军问我们“学生请教问题的图会上官网吗?”得到我们肯定的回答后,他高兴地回头对学生说:“听见了吗?你要上官网了。”这位女同学有些害羞,又好像对这个老师有些“无奈”。或许蒋学军与学生之间的这种亲和力,也是让他受到学生爱戴的原因。


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The Department of Statistics and Data Science was established in April 2019. As a newly established department, we currently have 8 faculty members including 1 Chair Professor, 2 Professors, 2 Associate Professors , 2 Tenure-track Assistant Professors and 1 Visiting Assistant Professor and anticipant to rapidly expand to a team of more than 20 members. All faculty members of the department have extensive overseas study or work experiences. One member is an invited speaker at the International Congress of Mathematicians, an IMS Medallion Lecturer and a winner of the prestigious State Natural Science Award (2nd class). The department has 2 research directions, Statistics and Data Science, which cover a broad array of research areas including Biostatistics, Financial Statistics, Experiment Design, Clinical Trials, High Dimensional Data, Time series, Probability Theory, Data Science and Big Data Technology.


At present, the department has an undergraduate program as well as two graduate programs(M.Phil and PhD). The department is in the process of developing a Major Program in Data Science and Big Data Technology. In 2019, the first batch of 38 undergraduate students in Statistics received the bachelor degree from SUSTech. 5 M.Phil students in Statistics also graduated in the same year. There are currently 9 M.Phil students, 7 PhD students in Statistics and 2 Postdoctoral Fellows. As the department grows in size and strength, the number of graduate students is expected to rise substantially from the current level and the areas of graduate studies will also expand to include Data Science and Big Data Technology.


This is the age of big data which offers exciting opportunities to researchers in Statistics and Data Science. Our department is expected to grow rapidly in the coming years. We invite outstanding scholars to join us in our journey to become a first-class department in Statistics and Data Science. We also welcome brilliant undergraduate and graduate students to apply for our programs. At the same time, we actively recruit excellent postdoctoral candidates.

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Room 522, Hui Yuan 3#

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