Ph.D., in Statistics, Duke University

Sheng Jiang is currently an Assistant Professor (tenure-track) in the School of Data Science, Chinese University of Hong Kong, Shenzhen. His research interest is generally on the theme of Bayesian (nonparametric) theory and methods to identify structural information of data. In particular, he is passionate about studying the uncertainty quantification of variational Bayes, and Bayesian hierarchical mixture-of-experts models to model oceanographic flow cytometry data. His previous work on Bayesian nonparametric methods with Gaussian process priors has been published in leading statistics journals such as AOS and JASA. Before moving to Shenzhen, he was a Visiting Assistant Professor in the Department of Statistics at the University of California Santa Cruz for 2.5 years, and a post-doctoral associate co-advised by Surya Tokdar and Alexander Volfovsky for one semester at Duke University where he also completed a Ph.D. in Statistics under the supervision of Surya Tokdar.

1. Surya T Tokdar, Sheng Jiang, and Erika L Cunningham. (2024) Heavy-tailed density estimation. Journal of the American Statistical Association. 119 (545): 163-175

2. Sheng Jiang, and Surya T Tokdar. (2021) Variable selection consistency of Gaussian process regression. The Annals of Statistics 49(5):2491–2505.

3. Rachel M Coyte, Kristen L McKinley, Sheng Jiang, Jonathan Karr, Gary S Dwyer, Amy J Keyworth, Christina C Davis, Andrew J Kondash, and Avner Vengosh. (2020) Occurrence and distribution of hexavalent chromium in groundwater from North Carolina, USA.Science of the Total Environment, 711:135135.

4. Yi Chen, Sheng Jiang, and Li-An Zhou. Estimating returns to education in urban China: Evidence from a natural experiment in schooling reform.Journal of Comparative Economics, 48(1):218–233.