Runqi Lin

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Biography

I am currently a Postdoctoral Researcher at Oxford Internet Institute, University of Oxford, hosted by Prof. Chris Russell. Prior to this, I completed my doctoral studies at the University of Sydney in January 2026, and was fortunate to be advised by Prof. Tongliang Liu. I was a visiting student at the University of Oxford, MBZUAI (Mohamed bin Zayed University of Artificial Intelligence), and Tsinghua University. My research interests lie in human-centred AI, with a particular emphasis on trustworthy and responsible machine learning.

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Publications

      * indicates equal contribution;   † = indicates indicates corresponding author.

• FORCE: Transferable Visual Jailbreaking Attacks via Feature Over-Reliance CorrEction.
  Runqi Lin, Alasdair Paren, Suqin Yuan, Muyang Li, Philip Torr, Adel Bibi^†, and Tongliang Liu^†.
  CVPR 2026. [PDF] [CODE]

• Mobile-VTON: High-Fidelity On-Device Virtual Try-On.
  Zhenchen Wan^*, Ce Chen^*, Runqi Lin, Jiaxin Huang, Tianxi Chen, Yanwu Xu, Tongliang Liu^†, and Mingming Gong^†.
  CVPR 2026. [PDF] [CODE]

• Understanding and Enhancing the Transferability of Jailbreaking Attacks.
  Runqi Lin, Bo Han, Fengwang Li, and Tongliang Liu^†.
  ICLR 2025. [PDF] [CODE]

• Instance-dependent Early Stopping.
  Suqin Yuan, Runqi Lin, Lei Feng^†, Bo Han, and Tongliang Liu^†.
  ICLR 2025 (Spotlight, 5.1%). [PDF] [CODE]

• Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency.
  Runqi Lin, Chaojian Yu, Bo Han, Hang Su, and Tongliang Liu^†.
  ICML 2024. [PDF] [CODE]

• On the Over-Memorization During Natural, Robust and Catastrophic Overfitting.
  Runqi Lin, Chaojian Yu, Bo Han, and Tongliang Liu^†.
  ICLR 2024. [PDF] [CODE]

• Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization.
  Runqi Lin, Chaojian Yu, and Tongliang Liu^†.
  NeurIPS 2023. [PDF] [CODE]

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Research Interests

• Fairness
  - Fairness in Generative Model.
  

• Adversarial Robustness
  - Adversarial Attack & Training.
  - Jailbreaking Attack in Large Language Models.
  - Robustness in Vision-Language Models.

• Generalization Capability
  - Catastrophic & Robust Overfitting.
  - Sharpness & Transferability.

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Honors & Awards

• USYD Faculty of Engineering Career Advancement Award.
  

• UAI 2025 Top Reviewer.
  

• ACM MM 2024 Outstanding Reviewer.
  

• OpenAI Researcher Access Program.
  

• ICML 2024 Financial Aid.
  

• NeurIPS 2023 Scholar Award.
  

• USYD Faculty of Engineering Research Scholarship.
  

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Academic Services

• Organising Committee: Local Arrangement Chair at International Symposium on Trustworthy Foundation Models.
  

• Conference Reviewer: NeurIPS, ICML, ICLR, CVPR, ECCV, AAAI, IJCAI, UAI, AISTATS, ACM MM, ACM SIGKDD, etc.
  

• Journal Reviewer: TPAMI, TIP, MLJ, TMLR, JAIR, etc.