- WWW'25: AI Model Modulation with Logits Redistribution
- OOPSLA'25: Convex Hull Approximation for Activation Functions
- S&P'24: CoreLocker: Neuron-level Usage Control
- TCSS: Data Hiding With Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Awards:
- Google PhD Fellowship, 2024
- Supported by RTP Scholarship and CSIRO’s Data61 Top-up Scholarship
Research Experience
Serving as the HDR representative for the CSS discipline. Involved in multiple research projects including AI model usage control and neural network verification.
Education
Graduated from the University of Adelaide with a B.CS (Adv.) in 2022. Currently a Ph.D. student at the University of Queensland, supervised by A/Prof. Guangdong Bai and Dr. Jason Xue.
Background
Research interests: tackling real-world security & privacy issues of ML systems in a formally verifiable manner. Works closely with colleague Zhongkui Ma on neural network verification (NNV). Current projects include AI Model Usage Control, Neuron-level Usage Control (CoreLocker), Logit-level Model Modulation (AIM), Neural Network Verification (WRAACT), Algorithmic Purpose Limitation (AlgoSpec), Token-level Gradient Inversion (Grab), Multi-modal Membership Inference (M⁴I), Model Pruning (CoreLocker), and Knowledge Distillation (BPKD). Currently serving as the HDR representative for the CSS discipline.