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Dr. HE Jingyu


Assistant Professor

7-278, Lau Ming Wai Academic Building, City University of Hong Kong
+852 34424753
+852 34420189
Public CV


PhD - Econometrics and Statistics (The University of Chicago Booth School of Business)
MBA - Business Administration (The University of Chicago Booth School of Business)
MS - Statistics (The University of Chicago)
BS - Statistics (University of Science and Technology of China)


Dr. He is an assistant professor of business statistics at the City University of Hong Kong. He received Ph.D., M.B.A. and M.S. from The University of Chicago, and B.S. from University of Science and Technology of China. His research interests include Bayesian statistics, machine learning algorithm and quantitative finance.


Journal Publications and Reviews

Feng, Guanhao; He, Jingyu / Factor investing: A Bayesian hierarchical approach. September 2022; In: Journal of Econometrics. Vol. 230, No. 1, pp. 183-200

He, Jingyu; Hahn, P. Richard / Stochastic tree ensembles for regularized nonlinear regression. June 2021; In: Journal of the American Statistical Association.

Hahn, P. Richard; He, Jingyu; Lopes, Hedibert F. / Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors. 2019; In: Journal of Computational and Graphical Statistics. Vol. 28, No. 1, pp. 142-154

Hahn, P. Richard; He, Jingyu; Lopes, Hedibert / Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments. April 2018; In: Journal of Business and Economic Statistics. Vol. 36, No. 2, pp. 278-287

Hahn, P. Richard; Carvalho, Carlos M.; Puelz, David; He, Jingyu / Regularization and Confounding in Linear Regression for Treatment Effect Estimation. 2018; In: Bayesian Analysis. Vol. 13, No. 1, pp. 163-182

Working Papers

Feng, Guanhao; He, Jingyu / Factor Investing: A Bayesian Hierarchical Approach. February 2019;

Chapters, Conference Papers, Creative and Literary Works

He, Jingyu; Yalov, Saar; Hahn, P. Richard / XBART: Accelerated Bayesian Additive Regression Trees. April 2019; The 22nd International Conference on Artificial Intelligence and Statistics. pp. 1130-1138

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