Ph.D. candidate / RMIT University

Jiachen Li.

Securing brain-inspired AI.

My research explores the security and trustworthiness of Spiking Neural Networks (SNNs), a new generation of energy-efficient brain-inspired AI models.

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SNN securityBackdoor attacks & defensesFederated learning

Jiachen Li speaking at a lectern — RMIT UNIVERSITY School of Computing Technologies

RESEARCH

Selected publications

RAID 2025Conference paper

Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks

Jiachen Li, Bang Wu, Xiaoyu Xia, Xiaoning Liu, Xun Yi, and Xiuzhen Zhang

Proceedings of the 28th International Symposium on Research in Attacks, Intrusions and Defenses (RAID), 2025

PDF Code Poster DOI

@inproceedings{li2025unsupervised,
  author = {Li, Jiachen and Wu, Bang and Xia, Xiaoyu and Liu, Xiaoning and Yi, Xun and Zhang, Xiuzhen},
  title = {Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks},
  booktitle = {Proceedings of the 28th International Symposium on Research in Attacks, Intrusions and Defenses (RAID)},
  year = {2025},
  address = {Gold Coast, Australia},
  publisher = {IEEE},
  doi = {10.1109/RAID67961.2025.00025},
  url = {https://arxiv.org/abs/2510.06629},
  eprint = {2510.06629},
  archivePrefix = {arXiv},
  primaryClass = {cs.CR}
}

BACKGROUND

A little about me

I am a Ph.D. candidate at the School of Computing Technologies, RMIT University, advised by Professor Xun Yi, Professor Xiuzhen (Jenny) Zhang, Dr. Xiaoning (Maggie) Liu, Dr. Xiaoyu (Shaw) Xia, and Dr. Bang Wu.

Specifically, I study backdoor attacks, detection, and mitigation techniques under both centralized and federated learning frameworks. My goal is to develop robust, theoretically grounded defenses that require minimal assumptions about the defender’s knowledge, making them more practical for real-world deployment.

Before starting my PhD, I completed a Master of Computer Science and Bachelor of Science at The University of Melbourne.

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