Michael Minyi Zhang
Michael Zhang is currently an assistant professor since January 2021 in the School of Computing and Data Science at the University of Hong Kong. His research interests include statistical machine learning, scalable inference and Bayesian non-parametrics. Michael Zhang was a post-doctoral researcher at Princeton University under the supervision of Profs. Barbara Engelhardt and Brandon Stewart and earned a Ph.D. in statistics at the University of Texas at Austin where he was advised by Prof. Sinead Williamson.
News
- Revisiting Nonstationary Kernel Design for Multi-Output Gaussian Processes and A Bayesian Nonparametric Framework for Private, Fair, and Balanced Tabular Data Synthesis has been accepted to ICLR 2026 as a poster.
- A Bayesian Bootstrap Framework for Mutual Information Neural Estimation is now published in TMLR.
- Ying Li has successfully defended her dissertation. Congratulations!
- I will serve as area chair at AISTATS 2026, ICML 2026 and NeurIPS 2026.
- I am now an action editor at Transactions on Machine Learning Research.
- Multi-View Oriented GPLVM: Expressiveness and Efficiency has been accepted to NeurIPS 2025.
- Scalable Random Feature Latent Variable Models has been published in IEEE TPAMI.
- I will be visiting University College London during summer 2025 thanks to support from the Sino-British Fellowship Trust.
- Online Student-t Processes with an Overall-local Scale Structure for Modelling Non-stationary Data has been published in AISTATS 2025.