Yu Yao

Yu Yao is a Lecturer in Machine Learning at the School of Computer Science, The University of Sydney. He joined in December 2023 and focuses on developing robust and interpretable machine learning systems. His research emphasizes robustness to data noise, adaptable ML systems, and disentangled representation learning. Yao holds a PhD from The University of Sydney under Professors Tongliang Liu and Dacheng Tao, followed by postdoctoral positions at Mohamed bin Zayed University of Artificial Intelligence and Carnegie Mellon University.

Education: PhD in Computer Science (University of Sydney), postdoctoral research at MBZUAI and CMU. Research interests include causal inference in ML, multimodal learning, and label noise mitigation. He has published extensively in top venues like ICML, NeurIPS, and ICLR, and served as an Area Chair for AJCAI 2023, NeurIPS 2025, and ICLR 2025.

• Awards: Outstanding Reviewer (NeurIPS 2023, ICLR 2023), University of Sydney Research Excellence Prize (2019)
• Teaching: Advanced Machine Learning (USYD), Guest Lectures on noisy label learning (MBZUAI, China University of Petroleum)
• Service: Action Editor for TMLR, Area Chair for ICML/ICLR/NeurIPS, reviewer for top journals and conferences

His lab focuses on trustworthy AI, with ongoing projects on causal mechanisms in robust learning and interpretable multimodal systems. Current advisees include PhD candidates Ruojing Dong and Jiyang Zheng (co-advised with Prof. Liu), and master's student Kai Lian.