Research Areas

Machine Learning, Tree Ensembles, Bayesian Statistics, Empirical Asset Pricing.


    * indicates alphabetical order.

  1. Jingyu He, Saar Yalov, Jared Murray and P. Richard Hahn (2019). Stochastic tree ensembles for regularized supervised learning. Job Market Paper.

  2. Abstract: This paper introduces a novel stochastic tree ensemble method for regression and clas- sification (i.e. supervised machine learning). By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques for recursive parti- tioning, the new method attains state-of-the-art performance: in many settings it is both faster and more accurate than the widely-used XGBoost algorithm.

  3. Guanhao Feng and Jingyu He* (2018). Factor Investing: Hierarchical Ensemble Learning. Submitted.

  4. Christopher Glynn, Jingyu He*, Nicholas Polson and Jianeng Xu (2019). Bayesian Inference for Polya Inverse Gamma Models. Submitted.

  5. Guanhao Feng, Jingyu He* and Nicholas G. Polson (2018). Deep Learning for Predicting Asset Returns. Working paper.

  6. Jingyu He, Saar Yalov and P. Richard Hahn (2019). XBART: Accelerated Bayesian Additive Regression Trees. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). slides, poster, package: XBART.

  7. P. Richard Hahn, Jingyu He and Hedibert Lopes (2019). Efficient sampling for Gaussian linear regression with arbitrary priors. Journal of Computational and Graphical Statistics 28.1 (2019): 142-154. R package : bayeslm, Demo Script.

  8. P. Richard Hahn, Carlos M. Carvalho, Jingyu He and David Puelz (2018). Regularization and confounding in linear regression for treatment effect estimation. Bayesian Analysis 13 (1), 163-182.

  9. P. Richard Hahn, Jingyu He and Hedibert Lopes (2018). Bayesian factor model shrinkage for linear IV regression with many instruments. Journal of Business and Economic Statistics 36 (2), 278-287.

Working in Progress

  1. Hierarchical Mixture Models with Elliptical Slice Sampling, with Sanjog Misra and Peter Rossi.

  2. Deep Learning Particle Filtering, with Nicholas Polson and Yuexi Wang.


Journal of Econometrics, Jounral of Empirical Finance.

Invited Seminars

10/2019   Arizona State University, Department of Statistics.

Conference Talks

05/2020   ICSA 2020 Applied Statistics Symposium, Houston.
07/2019   China International Conference in Finance, Guangzhou.
06/2019   Asia Meeting of the Econometric Society (2019 AMES), Xiamen.
06/2019   NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics, Brown University.
05/2019   China R Conference, Renmin University.
05/2019   R in Finance, University of Illinois at Chicago.
04/2019   International Conference on FinTech, Shanghai Jiao Tong University.
03/2019   Econometrics and Statistics Lunch Seminar, University of Chicago Booth School of Business.
07/2017   Joint Statistical Meetings, Baltimore.
06/2016   International Society of Bayesian Analysis World Meeting, Sardinia, Italy.
04/2016   NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics, Wharton.