Bio
I am an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign. I 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.
My work is supported by NSF DMS. Thanks NSF!
Interests
- Generative Models
- Deep Learning
- Approximate Bayesian Inference
- 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
Publications
(2026).
An Optimal Transport-Based Generative Model for Bayesian Posterior Sampling.
Journal of the Royal Statistical Society, Series B.
(2026).
Invited Discussion of of ‘Robust Probabilistic Inference via a Constrained Transport Metric’.
Bayesian Analysis.
(2026).
Generative Bayesian Inference with GANs.
Journal of Machine Learning Research.
(2026).
Likelihood-free Inference via Structured Score Matching.
Conference on Artificial Intelligence and Statistics (AISTATS).
(2023).
Data Augmentation for Bayesian Deep Learning.
Bayesian Analysis.
Contact
- yxwang99[at]illinois[dot]edu