I am a PhD student in Econometrics and Statistics at the University of Chicago, Booth School of Business.

Research Interests

I am a computational statistician by training and I am interested in financial econometrics, machine learning (Deep Neural Networks, Reinforcement Learning, Artificial Intelligence), and numerical optimization.



Working Paper: “Knowing Factors or Factor Loadings, or Neither? Evaluating Estimators of Large Covariance Matrices with Noisy and Asynchronous Data”,with Kun Lu and Dacheng Xiu, Journal of Econometrics 208(2019),43-79


Teaching Assistant


Python: I mainly use Python for deep learning and reinforcement learning, replying on numpy and TensorFlow, among others. I design the architectures and build the computational graph from scratch, utilizing features like two passes, dynamic indexing and exotic loss functions.

MATLAB: This is my primary language for numerical computation, massive data manipulation, optimization and visualization.

R: I assisted in developing the ashr package [https://github.com/stephens999/ashr] for Adaptive Shrinkage method, focusing on numerical optimization, parallel computation, automatic testing & deployment.

I am a heavy user of Booth Research Grid and Research Computing Center. I test my codes locally and deploy them to the cluster for massive parallel computation.

Contact Information

Email is the best way to reach me: chaoxingdai at chicagobooth.edu

Address:The University of Chicago Booth School of Business 5807 S Woodlawn Ave, Chicago, IL 60637