Prof. HE Jingyu
何靖宇教授
Assistant Professor
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)
+852 34424753
Fax
+852 34420189
Office
LAU-7-278
Email
Personal Web
Biography
Dr. 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 machine learning in finance, empirical asset pricing, and Bayesian statistics. His research work has appeared in leading statistics, finance and econometrics journals.
Awards
| Award Title | Institution |
|---|---|
| 2022 INQUIRE Europe Research Grant Award | INQUIRE Europe |
Research Grant
PI: "Regression Tree for Portfolio Optimization and Imbalanced Data", General Research Fund - HKRGC, (2023-2025), Jingyu He
PI: "What Stocks are Predictable by Machine Learning? Find Heterogenity of Stocks by Firm Characteristics", Strategic Research Grant - City University of Hong Kong, (2023-2025), Jingyu He
PI: "XBART: A Novel Tree-Based Machine Learning Framework for Regression, Classification and Treatment Effect Estimation", Early Career Scheme - HKRGC, (2022-2023), Jingyu He
PI: "Elliptical Slice Sampler for Hierarchical Models in Marketing", Start-Up Grant - City University of Hong Kong, (2021-2023), Jingyu He
Publications
Paul, Erina; He, Jingyu; Mallick, Himel / Accelerated Bayesian Reciprocal LASSO. November 2023; In: Communications in Statistics: Simulation and Computation.
Wang, Meijia; He, Jingyu; Hahn, P. Richard / Local Gaussian process extrapolation for BART models with applications to causal inference. July 2023; In: Journal of Computational and Graphical Statistics.
Feng, Guanhao; He, Jingyu; Polson, Nick G.; Xu, Jianeng / Deep Learning in Characteristics-Sorted Factor Models. July 2023; In: Journal of Financial and Quantitative Analysis.
He, Jingyu; Hahn, P. Richard / Stochastic tree ensembles for regularized nonlinear regression. March 2023; In: Journal of the American Statistical Association. Vol. 118, No. 541, pp. 551–570
Feng, Guanhao; He, Jingyu / Factor investing: A Bayesian hierarchical approach. September 2022; In: Journal of Econometrics. Vol. 230, No. 1, pp. 183-200
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
Cong, Lin William; Feng, Guanhao; He, Jingyu; Li, Junye / Uncommon Factors for Bayesian Asset Clusters. September 2022;
Cong, Lin William; Feng, Guanhao; He, Jingyu; He, Xin / Growing the Efficient Frontier on Panel Trees. October 2021;
Krantsevich, Nikolay; He, Jingyu; Hahn, P. Richard / Stochastic Tree Ensembles for Estimating Heterogeneous Effects. April 2023; Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023. Vol. 206, pp. 6120-6131
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