Jiang Xuejun

Jiang Xuejun is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). He has been with SUSTech since 2013, initially as a Tenure-Track Assistant Professor and promoted to Associate Professor in 2019. Prior to joining SUSTech, he served at Zhongnan University of Economics and Law.

His educational background includes:

• Ph.D. in Statistics from The Chinese University of Hong Kong (2009)
• M.Sc. from Yunnan University
• B.Sc. from National University of Defense Technology

Jiang Xuejun's research focuses on advanced statistical methodologies with applications in various domains. His work spans statistical theory development and practical applications in finance, economics, and risk assessment. He has made significant contributions to quantile regression, variable selection, survival analysis, and nonparametric regression methods.

His research publications demonstrate a strong trend toward developing innovative statistical methods for high-dimensional data analysis, Bayesian modeling approaches, and applications in financial econometrics and disaster risk assessment. Many of his recent papers focus on quantile regression techniques, dimension reduction methods, and robust statistical testing procedures.

Jiang Xuejun has received several prestigious awards:

• Shenzhen Outstanding Teacher (2018)
• Southern University of Science and Technology "Outstanding Teaching Award" (2018)
• "Excellent Mentor Award" from Southern University of Science and Technology (2016)
• Selected for Shenzhen's "Peacock Plan" for overseas high-level talents

He has successfully secured multiple research grants as Principal Investigator, including projects funded by the National Natural Science Foundation of China (both General and Youth programs), Guangdong Provincial Natural Science Foundation, and Shenzhen Science and Technology Innovation Commission. His research portfolio includes work on likelihood inference for high-dimensional models, statistical methods for epidemic disease control, and quantitative trading systems using machine learning.

Jiang maintains an active research group focusing on statistical methodology development and applications, with particular emphasis on financial statistics and econometrics. His team collaborates with researchers across multiple disciplines to address complex data analysis challenges in economics, finance, and public health.