WraLUPOPL'24
ReLU Hull Approximation
Approximate ReLU hulls by reusing their linear pieces and constructing enclosing upper faces, rather than building the exact hull.
PhD Candidate · Trustworthy AI Research
Hi, I'm Zhongkui Ma
Diving deep into my PhD journey at The University of Queensland.
I study the verification, security, and privacy of AI systems, with a focus on neural network verification and convex approximation.
Selected research
Selected work in verification, model control, and privacy.
WraActOOPSLA'25
Convex Hull Approximation for Activation Functions
Use double-linear-piece functions to simplify local activation geometry and construct convex over-approximations beyond ReLU.
CoreLockerS&P'24
CoreLocker: Neuron-level Usage Control
Lock a network behind a small subset of its own significant weights, so holding the key gives the whole capability and not holding it gives part of it.
GhostAsia CCS'26
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
Defend collaborative training against gradient inversion by substituting tokens that are semantically distinct but close in embedding space.
Explore how these projects connect across my research, or browse the complete publication list.
Tutorial · A series in progress
A Guide to Neural Network Verification
29 chapters in 4 parts, from what a network can be asked to prove to how the relaxation is built and solved. Written to be read in order.
News
Acceptances, awards and releases.
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Sept. 2026Paper Accepted
Our paper Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization is accepted by NeurIPS'26 as an oral presentation (112 of 30709 submissions, about 0.36%). Congrats, Zihan, Ethan and Zhongkui!
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Aug. 2026Best Paper Award
Our paper Non-Transferable Examples receives the Best Paper Award - Runner Up at the ECCV'26 LifeGenIP Workshop. Congrats, Zihan!
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Mar. 2026Paper Accepted
Our paper Re-Key-Free, Risky-Free: Adaptable Model Usage Control is accepted by Euro S&P'26. Congrats, Zihan!
6 earlier entries
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Nov. 2025Paper Accepted
Our paper Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation is accepted by Asia CCS'2026. Congrats, Xinguo!
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Aug. 2025Paper Accepted
Our paper Convex Hull Approximation for Activation Functions is accepted by OOPSLA'25 within SPLASH'25. Happy!
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Jan. 2025Paper Accepted
Our paper AI Model Modulation with Logits Redistribution is accepted by WWW'25. Congrats, Zihan!
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Aug. 2024Paper Accepted
Our paper Uncovering Gradient Inversion Risks in Practical Language Model Training is accepted by CCS'24. Congrats, Xinguo!
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Apr. 2024Paper Accepted
Our paper CORELOCKER: Neuron-level Usage Control is accepted by S&P'24. Congrats, Zihan! [Live Video]
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Nov. 2023Paper Accepted
Our paper ReLU Hull Approximation is accepted by POPL'24.
Writing
Notes on the tools behind the work.
ShapeONNX: Solving ONNX's Dynamic Shape Problem
A dual-track shape inference tool that resolves ONNX's dynamic shapes to concrete static values for neural network verification workflows.
January 2026SlimONNX: A Story of Optimizing Neural Networks for Verification
A pure Python toolkit for optimizing ONNX models specifically for verification workflows, validated on the VNN-COMP 2024 benchmark suites.
January 2026