Lijie Hu
Assistant Professor of Machine Learning
Research Interests
Professor Hu's research focuses on responsible AI, particularly in explainable AI (XAI) and privacy-preserving machine learning. Her recent research emphasizes making XAI more accessible and practical. Her work centers on developing Usable XAI-as-a-Service systems (Usable XAI) and Useful Explainable AI toolkits (Useful XAI), bridging the gap between theoretical innovation and real-world application.
Email
Prior to joining MBZUAI, Professor Hu completed her Ph.D. in Computer Science at King Abdullah University of Science and Technology (KAUST). Her research focuses on responsible AI, with particular emphasis on explainable AI (XAI) and privacy-preserving machine learning.
Her recent work aims to make XAI more accessible and applicable in practice. She has introduced concepts such as Usable XAI-as-a-Service systems and Useful Explainable AI toolkits, bridging the gap between theoretical advances and real-world implementation.
Professor Hu’s research was recognized as a "Best of PODS 2022" selection. She has also received several honors, including the KAUST Dean’s List Award and recognition as a Top Reviewer at AISTATS 2023. In addition to her research, she contributes to the broader academic community as a member of the AAAI Student Committee.
Her recent work aims to make XAI more accessible and applicable in practice. She has introduced concepts such as Usable XAI-as-a-Service systems and Useful Explainable AI toolkits, bridging the gap between theoretical advances and real-world implementation.
Professor Hu’s research was recognized as a "Best of PODS 2022" selection. She has also received several honors, including the KAUST Dean’s List Award and recognition as a Top Reviewer at AISTATS 2023. In addition to her research, she contributes to the broader academic community as a member of the AAAI Student Committee.
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Ph.D. in computer science from King Abdullah University of Science and Technology (KAUST)
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Master of Science in Mathematics from Renmin University of China
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Bachelor of Science in Mathematics from Minzu University of China
- Best of PODS 2022
- King Abdullah University of Science and Technology (KAUST) Dean’s List Award in 2022 and 2024
- Top Reviewer AISTATS 2023.
Professor Hu's research publications include:
- Zhuoran Zhang, Yongxiang Li, Zijian Kan, Keyuan Cheng, Lijie Hu, Di Wang. "Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing." ICML 2025.
- Lijie Hu , Chenyang Ren, Zhengyu Hu, Hongbin Lin, Cheng-Long Wang, Zhen Tan, Weimin Lyu, Jingfeng Zhang, Hui Xiong, Di Wang. "Editable Concept Bottleneck Models.” ICML 2025.
- Lijie Hu, Tianhao Huang, Huanyi Xie, Xilin Gong, Chenyang Ren, Zhengyu Hu, Lu Yu, Ping Ma, Di Wang. "Semi-supervised Concept Bottleneck Models." ICCV 2025.
- Lijie Hu, Songning Lai, Yuan Hua, Shu Yang, Jingfeng Zhang, Di Wang. "Stable Vision Concept Transformers for Medical Diagnosis." ECML-PKDD 2025.
- Jia Li, Lijie Hu, Jingfeng Zhang, Tianhang Zheng, Hua Zhang, and Di Wang. "Fair Text-to-Image Diffusion via Fair Mapping.” AAAI 2025, Oral.
- Lijie Hu, Tianhao Huang, Lu Yu, Wanyu Lin, Tianhang Zheng, and Di Wang. "Faithful Interpretation for Graph Neural Networks." Transactions on Machine Learning Research.
- Lijie Hu, Xinhai Wang, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, and Di Wang. "Towards Stable and Explainable Attention Mechanisms." IEEE Transactions on Knowledge and Data Engineering.
- Lijie Hu, Songning Lai, Wenshuo Chen, Hongru Xiao, Hongbin Lin, Lu Yu, Jingfeng Zhang, Di Wang. "Towards Multi-dimensional Explanation Alignment for Medical Classification." NeurIPS 2024.
- Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, and Di Wang. "Improving Interpretation Faithfulness for Vision Transformers." ICML 2024, Spotlight.
- Songning Lai, Lijie Hu, Junxiao Wang, Laure Berti-Equille, and Di Wang. "Faithful Vision-Language Interpretation via Concept Bottleneck Models.” ICLR 2024.