Bo Han

Bo Han is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science, where he leads the Trustworthy Machine Learning and Reasoning (TMLR) Group. He also holds a visiting scientist position at the RIKEN Center for Advanced Intelligence Project (RIKEN AIP) in Japan. His research focuses on developing trustworthy and efficient machine learning systems, particularly under imperfect data conditions such as noisy labels, out-of-distribution data, and weak supervision.

Bo Han's research interests span Machine Learning, Deep Learning, Foundation Models, Causal Representation Learning, Weakly and Self-supervised Learning, Robustness and Security in Machine Learning, Federated Learning, and AI for Science. His work aims to build intelligent systems that can reliably learn and reason from complex, imperfect real-world data.

His recent publications reveal a strong trend toward trustworthy foundation models, robust reasoning with large language models, out-of-distribution detection, privacy-preserving learning, and causal robustness. His research integrates theoretical foundations with practical applications, often published in top-tier venues like NeurIPS, ICML, ICLR, and TPAMI.

• Notable Awards and Honors:
• Outstanding Paper Award, NeurIPS
• Most Influential Paper, NeurIPS
• IEEE AI's 10 to Watch Award
• IJCAI Early Career Spotlight
• INNS Aharon Katzir Young Investigator Award
• Dean's Award for Outstanding Achievement
• RGC Early CAREER Scheme

Bo Han has been actively involved in the academic community, serving as a Senior Area Chair and Area Chair for NeurIPS, ICML, and ICLR, and as an Associate Editor for IEEE TPAMI, MLJ, and JAIR. He has advised numerous PhD and research students and leads a globally distributed research group. His work is supported by major grants from RGC, NSFC, GDST, RIKEN, and industry partners including Microsoft, Alibaba, Tencent, and Baidu.

He also leads research initiatives in Trustworthy Machine Learning, including projects on federated learning, model unlearning, privacy-preserving AI, and robust foundation models, often in collaboration with industry and international institutions.