Yingbin Bai

Yingbin Bai is a Research Fellow at Australian National University's School of Computing, collaborating with Professor Sylvie Thiebaux. His research focuses on machine learning applications in planning, weakly supervised learning, and unsupervised representation learning. He holds a PhD supervised by Professors Tongliang Liu and Dadong Wang.

His work emphasizes addressing challenges in noisy label environments, early stopping techniques, and biomedical image analysis. Key contributions include QUBIQ (uncertainty quantification in medical imaging) and frameworks for robust representation learning under imperfect data conditions.

Publications span topics like symmetry breaking in learning systems, semantic shift mitigation in self-supervised learning, and analysis of biomedical image competition practices. Collaborations involve cross-disciplinary teams tackling both theoretical and applied machine learning problems.