Muxing Li

About Me

I am Muxing Li, a Ph.D. candidate at Trustworthy Machine Learning and Reasoning Group (TMLR) in the Faculty of Engineering and Information Technology, the University of Melbourne, advised by Dr. Feng Liu and Dr. Zesheng Ye. Before that, I was a research assistant in Centre for Frontier AI Research (CFAR) of Agency for Science, Technology and Research (A*STAR). I am passionate about advancing the field of trustworthy machine learning.

Research Interests

• Computer Vision: image recognition, image generation, video captioning
• Machine Learning: Membership inference attack

Education

• Apr. 2024-present - the Unversity of Melbourne (UoM), Ph.D. in Engineering and IT, advised by Dr. Feng Liu and Dr. Zesheng Ye.
• Feb. 2018-Feb. 2022 - the Australian National University (ANU), B.Sc. in Advanced computing (Honours), advised by Dr. Yu Lin

Internship experience

• Feb. 2023- Feb. 2024 - The Agency for Science, Technology and Research (A*STAR), Research Assistant, advised by Prof. Joey Zhou and Dr. Ping Liu.

publication

• Lu M, Fang L, Li M, et al. NFANet: A novel method for weakly supervised water extraction from high-resolution remote-sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
• Li Z, Duan P, Hu S, et al. Fast hyperspectral image dehazing with dark-object subtraction model[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.
• Fang L, Guo J, He X, et al. Self-supervised patient-specific features learning for OCT image classification[J]. Medical & Biological Engineering & Computing, 2022, 60(10): 2851-2863.
• Li M, Ye Z, Li Y, et al. Membership Inference Attack Should Move On to Distributional Statistics for Distilled Generative Models[J]. arXiv preprint arXiv:2502.02970, 2025.

Academic Service

• Conference Reviewer: ICML, NeurIPS, ICLR, AISTATS, etc.
• Journal Reviewer: NEUNET, etc.

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