I am a Ph.D. candidate in Ecnometrics and Statistics at The University of Chicago Booth School of Business. My research focuses on machine learning algorithms, particularly tree ensembles, Bayesian statistics and empirical asset pricing. I have developed R package XBART for accelerated Bayesian additive regression trees, which provides fast computation and state-of-the-art prediction accuracy.

I will finish my Ph.D. in 4 years and be on the 2019-2020 job market.

Stochastic tree ensembles for regularized supervised learning. Job Market Paper.

Curriculum Vitae.pdf

E-mail: jingyu.he@chicagobooth.edu