Projects per year
Personal profile
Chinese Name
Biography
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
- Weakly Supervised Representation Learning
- Security, Privacy and Robustness in Machine Learning
- Automated, Federated and Graph Machine Learning
- Interdisciplinary Problems (e.g., Healthcare Analytics and Drug Discovery)
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
PhD, University of Technology Sydney
Feb 2015 → Feb 2019
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Collaborations and top research areas from the last five years
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Large Language Models for Knowledge Graph Reasoning & Dictionary Learning and Transfer Learning
1/12/24 → 30/11/25
Project: Research project
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分布外数据下的可信图表征学习研究 Trustworthy Graph Representation Learning under Out-of-distribution Data
HAN, B. (PI), ZHANG, Y. (CoI), WANG, Q. (CoI), ZHU, J. (CoI), ZHOU, Z. (CoI), JIANG, X. (CoI), TIAN, H. (CoI) & PENG, X. (CoI)
1/01/24 → 31/12/26
Project: Research project
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The Research on Trustworthy Federated Learning with Application to PaddlePaddle
HAN, B. (PI)
31/10/23 → …
Project: Research project
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New Directions in Trustworthy Machine Learning
HAN, B. (PI)
1/12/23 → 30/11/24
Project: Research project
Research Output
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Alignclip: navigating the misalignments for robust vision-language generalization
Han, Z., Luo, G., Sun, H., Li, Y., Han, B., Gong, M., Zhang, K. & Liu, T., Mar 2025, In: Machine Learning. 114, 3, 19 p., 58.Research output: Contribution to journal › Journal article › peer-review
Open Access -
A Robust Co-training Framework to Handle Noisy Labels for Remote Sensing Image Segmentation
Otsu, M., Zhou, J., Han, B., Accad, A. & Pan, S., 24 Apr 2025, (E-pub ahead of print) In: IEEE Transactions on Geoscience and Remote Sensing. 63, 15 p., 3000814.Research output: Contribution to journal › Journal article › peer-review
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Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation
Zhang, Y., Ye, G., Yuan, C., Han, B., Huang, L.-K., Yao, J., Liu, W. & Rong, Y., 24 Apr 2025, Proceedings of the Thirteenth International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, p. 1-29 29 p.Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review
Open Access -
Characterizing Submanifold Region for Out-of-Distribution Detection
Li, X., Fang, Z., Zhang, Y., Ma, N., Bu, J., Han, B. & Wang, H., Jan 2025, In: IEEE Transactions on Knowledge and Data Engineering. 37, 1, p. 130-147 18 p.Research output: Contribution to journal › Journal article › peer-review
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Defending Against Adversarial Examples Via Modeling Adversarial Noise
Zhou, D., Wang, N., Han, B., Liu, T. & Gao, X., 14 May 2025, (E-pub ahead of print) In: International Journal of Computer Vision. 18 p.Research output: Contribution to journal › Journal article › peer-review
Prizes
Activities
- 7 Editor/reviewer for publications (incl. CDCF T61 Journal Editor)
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IEEE Transactions on Pattern Analysis and Machine Intelligence (Journal)
HAN, B. (Associate editor)
Jun 2024 → …Activity: Publication peer-review/editorial work › Editor/reviewer for publications (incl. CDCF T61 Journal Editor)
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Machine Learning (Journal)
HAN, B. (Associate editor)
Aug 2023Activity: Publication peer-review/editorial work › Editor/reviewer for publications (incl. CDCF T61 Journal Editor)
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Journal of Artificial Intelligence Research (Journal)
HAN, B. (Associate editor)
Jul 2023Activity: Publication peer-review/editorial work › Editor/reviewer for publications (incl. CDCF T61 Journal Editor)
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IEEE Transactions on Neural Networks and Learning Systems (Journal)
HAN, B. (Associate editor)
Aug 2022Activity: Publication peer-review/editorial work › Editor/reviewer for publications (incl. CDCF T61 Journal Editor)
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Transactions on Machine Learning Research (Journal)
HAN, B. (Associate editor)
Mar 2022Activity: Publication peer-review/editorial work › Editor/reviewer for publications (incl. CDCF T61 Journal Editor)