Bio

I am a fifth-year Ph.D. student in the Econometrics and Statistics group at the University of Chicago Booth School of Business. My primary research interests are deep learning, approximate Bayesian inference, Bayesian nonparametrics and high-dimensional statistics. I am currently working with Prof. Veronika Rockova and Prof. Nick Polson.

I am on the 2022-2023 job market.

Interests

Deep Learning
Approximate Bayesian Inference
Bayesian Nonparametrics
High-dimensional Statistics
Quantitative Marketing

Education

PhD student in Econometrics and Statistics, 2018-now

University of Chicago Booth School of Business
MSc in Statistics, 2016-2018

University of Chicago
BSc in Mathematics and Applied Mathematics, 2012-2016

Zhejiang University

Working Paper

Yuexi Wang, Veronika Rockova (2022). Adversarial Bayesian Simulation.

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Publications

Yuexi Wang, Tetsuya Kaji, Veronika Rockova (2022). Approximate Bayesian Computation via Classification. Journal of Machine Learning Research.

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Yuexi Wang, Nicholas G Polson, Vadim Sokolov (2022). Data Augmentation for Bayesian Deep Learning. Bayesian Analysis.

PDF

Yi Liu, Veronika Rockova, Yuexi Wang (2021). Variable Selection with ABC Bayesian Forests. Journal of the Royal Statistical Society, Series B.

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Yuexi Wang, Veronika Rockova (2020). Uncertainty Quantification for Sparse Deep Learning. 23rd Conference on Artificial Intelligence and Statistics.

PhD student in Econometrics and Statistics

University of Chicago Booth School of Business

Bio

Interests

Education

Working Paper

Publications

Experience

Data Scientist Intern

Google Research

Data Science Intern

Conversant Media

Contact