TANG, Rong

TANG, Rong is an Assistant Professor in the Department of Mathematics at The Hong Kong University of Science and Technology. His research interests include machine learning theory, Bayesian statistics, sampling complexity, and nonparametric inference. He teaches MATH5432 Advanced Mathematical Statistics II. Tang has received the HKUST-POSTECH Joint Research Seed Grant Program & Global Knowledge Network Awards 2026 (2026). His publications appear in journals such as the Journal of Machine Learning Research, Annals of Statistics, and the Journal of the Royal Statistical Society Series B: Statistical Methodology, as well as in proceedings of conferences including ICLR 2025 and ML Research.

Rong Tang is a researcher at the Hong Kong University of Science and Technology, focusing on Bayesian inference, generative modeling, and statistical learning. Their work explores the adaptivity of diffusion models to manifold structures and the minimax rates for distribution estimation on unknown submanifolds. Tang's research also addresses adversarial losses in statistical learning and the computational complexity of Bayesian methods. They have contributed to the development of deep generative approaches for stratified learning and robust Bayesian inference on Riemannian submanifolds. Their publications highlight the intersection of nonparametric statistics, machine learning, and high-dimensional data analysis.