Dr. HAN, Bo
Dr. HAN, Bo

BEng, MPhil, PhD
Assistant Professor, Department of Computer Science


Bo Han is currently an Assistant Professor of Computer Science and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University, and a BAIHO Visiting Scientist at RIKEN Center for Advanced Intelligence Project (RIKEN AIP). He was a Visiting Faculty Researcher at Microsoft Research (2022) 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). During 2018-2019, he was a Research Intern with the AI Residency Program at RIKEN AIP, working on trustworthy representation learning (e.g., Co-teaching and Masking). He also works on causal representation learning (e.g., CausalAdv and CausalNL). He has co-authored a machine learning monograph, including Machine Learning with Noisy Labels (MIT Press). He has served as area chairs of NeurIPS, ICML and ICLR, senior program committees of AAAI, IJCAI and KDD, and program committees of AISTATS, UAI and CLeaR. He has also served as action (associate) editors of Transactions on Machine Learning Research, Neural Networks and IEEE Transactions on Neural Networks and Learning Systems, and editorial board members of Journal of Machine Learning Research and Machine Learning Journal. He received the RIKEN BAIHO Award (2019), RGC Early CAREER Scheme (2020), MSRA StarTrack Program (2021) and Tencent AI Focused Research Award (2022).

Research Interests

I am always looking for self-motivated PhD/RA/Visiting students and Postdoc researchers. Please read this document for recruiting information, and check this document for department information. Meanwhile, I am happy to host remote research trainees. Due to the large number of emails I receive, I cannot respond to every email individually. Thanks!

Selected Publications

(see the updated full list here)
  • Z. Tang, Y. Zhang, S. Shi, X. He, B. Han, and X. Chu. Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. In Proceedings of 39th International Conference on Machine Learning (ICML'22), 2022.
  • X. Peng, F. Liu, J. Zhang, J. Ye, L. Lan, T. Liu and B. Han. Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. In Proceedings of 28th ACM Conference on Knowledge Discovery and Data Mining (KDD'22), 2022.
  • 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 (PAMI), 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.
  • J. Zhang, B. Han, L. Wynter, B. Low, and M. Kankanhalli. Towards Robust ResNet: A Small Step but A Giant Leap. In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI'19), 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 (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 (NeurIPS'18), 2018. 
  • 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, 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, 2017.