معرفی
Yuexi Wang is an Assistant Professor in the Department of Statistics at the University of Illinois. Their research focuses on Bayesian methodology, approximate Bayesian computation, and deep learning applications in statistical inference. Key areas include uncertainty quantification, sparse deep learning, and statistical modeling for count data.
- Education: Not explicitly stated in text.
Research interests span Bayesian analysis, with emphasis on developing scalable methods for posterior approximation, adversarial simulation, and generative models. They have contributed to variable selection via Bayesian forests and uncertainty quantification in sparse neural networks. Recent work explores optimal transport-based methods for posterior sampling and Pochhammer priors in count models.
Publications emphasize methodological advances in Bayesian deep learning, including data augmentation techniques and adversarial approaches. Articles often bridge theory and application in machine learning and computational statistics.
No scientific awards explicitly mentioned. Advising and grants information unavailable in provided text.
