Bo Han is currently an Associate Professor and RGC Research Fellow in Machine Learning and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University, and a BAIHO Visiting Scientist of Imperfect Information Learning Team at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), where his research focuses on machine learning, deep learning, foundation models, and their applications. He was a Visiting Associate Professor at HKUST(GZ) DSA (2025), a Visiting Research Scholar at MBZUAI MLD (2024), a Visiting Faculty Researcher at Microsoft Research (2022) and Alibaba DAMO Academy (2021), and a Postdoc Fellow at RIKEN AIP (2019-2020). He received his Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019). He has co-authored three machine learning monographs, including Machine Learning with Noisy Labels (MIT Press), Trustworthy Machine Learning under Imperfect Data (Springer Nature), and Trustworthy Machine Learning from Data to Models (Foundations and Trends). He has served as Senior Area Chairs of NeurIPS and ICML, Area Chairs of ICLR, UAI and AISTATS, and Program Co-Chair of ACML. He has also served as Associate Editors of IEEE TPAMI, MLJ and JAIR, and Editorial Board Members of JMLR and MLJ. He is an ACM Distinguished Speaker, an IEEE Senior Member, and an HKAE Young Member. He received paper awards, including Outstanding Paper Award at NeurIPS, Most Influential Paper at NeurIPS, and Outstanding Student Paper Award at NeurIPS Workshop, and service awards, including Notable Area Chair at NeurIPS, Outstanding Area Chair at ICLR, Editorial Contribution Award at MLJ, and Outstanding Associate Editor at IEEE TNNLS. He received the RGC Research Fellow Award, RGC Early CAREER Scheme, NSFC AI Major Research Plan, IEEE AI's 10 to Watch Award, IJCAI Early Career Spotlight, INNS Aharon Katzir Young Investigator Award, IEEE Computing's Top 30 Early Career Professional Award, RIKEN BAIHO Award, Dean's Award for Outstanding Achievement, Microsoft Research StarTrack Scholars Program, and Faculty Research Awards from ByteDance, Baidu, Alibaba, Ant and Tencent.

• Foundation Models and Causal Representation Learning: [Monograph] [ICLR'24] [ICML'24] [NeurIPS'24] [ICLR'25] [ICML'25] [ICLR'26]
• Weakly Supervised and Self-supervised Representation Learning: [Survey] [NeurIPS'18] [ICML'19] [ICML'20] [ICML'21] [ICML'22] [ICLR'23]
• Robustness, Security and Privacy in Machine Learning: [NeurIPS'22] [ICLR'22] [NeurIPS'23] [NeurIPS'24] [ICLR'25] [ICLR'26]
• Federated, Efficient and Graph Machine Learning: [ICML'20] [KDD'21] [ICML'22] [NeurIPS'23] [ICLR'24] [ICLR'25]
• Interdisciplinary Problems (e.g., Healthcare Analytics and AI for Science): [NeurIPS'22] [Nature Plants'23] [ICLR'24] [NeurIPS'24] [ICLR'25]

(see the updated full list here)

• Q. Wang, B. Han, Y. Liu, C. Gong, T. Liu, and J. Liu. W-DOE: Wasserstein Distribution-agnostic Outlier Exposure. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
• Z. Zhou, R. Tao, J. Zhu, Y. Luo, Z. Wang, and B. Han. Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? In Advances in Neural Information Processing Systems 37 (NeurIPS'24), 2024.
• J. Yao, B. Han, Z. Zhou, Y. Zhang, and I.W. Tsang. Latent Class-Conditional Noise Model. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(8): 9964–9980, 2023.
• X. Xia, B. Han, N. Wang, J. Deng, J. Li, Y. Mao, and T. Liu. Learning with Mixed Open-set and Closed-set Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(3): 3047–3058, 2022.
• A. Berthon, B. Han, G. Niu, T. Liu, and M. Sugiyama. Confidence Scores Make Instance-dependent Label-noise Learning Possible. In Proceedings of 38th International Conference on Machine Learning (ICML'21), 2021.
• B. Han, G. Niu, X. Yu, Q. Yao, M. Xu, I.W. Tsang, and M. Sugiyama. SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. In Proceedings of 37th International Conference on Machine Learning (ICML'20), 2020.
• B. Han, Q. Yao, Y. Pan, I.W. Tsang, X. Xiao, Q. Yang, and M. Sugiyama. Millionaire: A Hint-guided Approach for Crowdsourcing. Machine Learning Journal (MLJ), 108(5): 831–858, 2019.
• B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I.W. Tsang, and M. Sugiyama. Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. In Advances in Neural Information Processing Systems 31 (NeurIPS'18), 2018.
• B. Han, J. Yao, G. Niu, M. Zhou, I.W. Tsang, Y. Zhang, and M. Sugiyama. Masking: A New Perspective of Noisy Supervision. In Advances in Neural Information Processing Systems 31 (NeurIPS'18), 2018. 
• B. Han, Y. Pan, and I.W. Tsang. Robust Plackett-Luce Model for k-ary Crowdsourced Preferences. Machine Learning Journal (MLJ), 107(4): 675–702, 2018.