Bo Han

Bo Han is currently an Associate Professor 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, and Area Chairs of ICLR, UAI and AISTATS. 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 and an IEEE Senior 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, and Outstanding Associate Editor at IEEE TNNLS. He received RGC Early CAREER Scheme, IEEE AI's 10 to Watch Award, IJCAI Early Career Spotlight, INNS Aharon Katzir Young Investigator Award, RIKEN BAIHO Award, Dean's Award for Outstanding Achievement, and Microsoft Research StarTrack Scholars Program.

ACM involvement:

Bo Han is an ACM member and an ACM Distinguished Speaker.

• Exploring Trustworthy Foundation Models under Imperfect Data
  In the current landscape of machine learning, it is crucial to build trustworthy foundation models that can operate under imperfect conditions, since most real-world data, such as unexpected...
• Exploring Trustworthy Foundation Models: Benchmarking, Finetuning and Reasoning
  In the current landscape of machine learning, where foundation models must navigate imperfect real-world conditions such as noisy data and unexpected inputs, ensuring their trustworthiness through...