Bio
I am an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign. I recently received my PhD in Econometrics and Statistics from the University of Chicago Booth School of Business, advised by Profs. Veronika Rockova and Nick Polson.
My research focuses on Bayesian theory and methodology, and their intersection with deep learning. In particular, my recent work examines simulation-based inference with generative AI models.
Interests
- Deep Learning
- Approximate Bayesian Inference
- Bayesian Nonparametrics
- High-dimensional Statistics
- Quantitative Marketing
Education
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PhD in Econometrics and Statistics and MBA, 2018-2023
University of Chicago Booth School of Business
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MSc in Statistics, 2016-2018
University of Chicago
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BSc in Mathematics and Applied Mathematics, 2012-2016
Zhejiang University
Working Paper
Publications
(2022).
Approximate Bayesian Computation via Classification.
Journal of Machine Learning Research.
(2022).
Data Augmentation for Bayesian Deep Learning.
Bayesian Analysis.
(2021).
Variable Selection with ABC Bayesian Forests.
Journal of the Royal Statistical Society, Series B.
(2020).
Uncertainty Quantification for Sparse Deep Learning.
23rd Conference on Artificial Intelligence and Statistics.
Contact
- yxwang99[at]illinois[dot]edu