Jiacheng (James) Zhang

Jiacheng (James) Zhang

PhD candidate at Unimelb

Hi there, I am Jiacheng (James) Zhang. I am an associate member of Sea AI Lab (SAIL), advised by Prof. Tianyu Pang, and a Ph.D. candidate at Trustworthy Machine Learning and Reasoning (TMLR) group in the Faculty of Engineering and Information Technology, the University of Melbourne, advised by Prof. Feng Liu and Prof. Ben Rubinstein. I received my Honours degree with a University Medal (Top 1 in major) from the University of Sydney, where I was fortunate to learn from Prof. Tongliang Liu. Prior to that, I earned my Bachelor’s degree from the University of Melbourne.

I am passionate about advancing the field of trustworthy machine learning, with a long-term vision of enabling safe, reliable, and ethically aligned AI systems that can be responsibly deployed in real-world environments. My current research interests lie in improving the robustness and safety of AI systems at multiple levels, including but not limited to: (1) safety alignment for multimodal large language models; (2) robust fine-tuning for pre-trained vision-language models; (3) robust training for vision models.

In an era where AI systems are increasingly embedded in high-stakes, real-world decision-making domains, I firmly believe that their foundations must be trustworthy and safe: not as an afterthought, but as a prerequisite for responsible and sustainable societal integration.

Please feel free to email me for research, collaborations, or a casual chat.

📖 Educations

  • 2023.08 - present, the Unversity of Melbourne (Unimelb), Ph.D. in Engineering and IT, advised by Prof. Feng Liu and Prof. Ben Rubinstein.
  • 2022.07 - 2023.07, the University of Sydney (USYD), B.Sc. in Data Science (Honours), advised by Prof. Tongliang Liu.
  • 2019.03 - 2022.03, the University of Melbourne (Unimelb), B.Sc. in Data Science.

📝 Publications

* Co-first author, ✉️ Corresponding author.

ICML 2024
sym

Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
Jiacheng Zhang, Feng Liu✉️, Dawei Zhou, Jingfeng Zhang, Tongliang Liu✉️.
In ICML 2024. [paper] [code]

ICML 2025
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One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
Jiacheng Zhang, Benjamin I.P. Rubinstein, Jingfeng Zhang, Feng Liu✉️.
In ICML 2025. [paper] [code]

ICML 2025
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Sample-specific Noise Injection for Diffusion-based Adversarial Purification
Yuhao Sun*, Jiacheng Zhang*, Zesheng Ye*, Chaowei Xiao, Feng Liu✉️.
In ICML 2025. [paper] [code]

🎖 Honours and Awards

  • 2025.06, ICML 2025 Travel Award.
  • 2025.05, ICML 2025 Top Reviewer (Top 2%).
  • 2025.03, Google Student Research Travel Scholarship.
  • 2024.06, ICML 2024 Travel Award.
  • 2023.07, Amazon Best UG Senior Project Award.
  • 2023.07, University Medal of USYD (Top 1 in Major, Highest Honour for Undergraduates).
  • 2023.06, Melbourne Research Scholarship of Unimelb.
  • 2021.02, Vacation Research Scholarship of Unimelb.

💻 Internships

✍️ Academic Services

  • Conference Reviewer for ICML, NeurIPS, ICLR, AISTATS, etc.
  • Journal Reviewer for NEUNET, IEEE TIFS, TMLR, etc.

💬 Invited Talks

  • 2025.01, Invited Talk on “Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training” @Unimelb, Melbourne.
  • 2024.11, Invited Talk on “Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training” @AJCAI 2024, Melbourne.

🧑‍🏫 Teaching Assistants

  • Teaching Assistant for SWEN20003: Object Oriented Software Development (2024 S2 / 2025 S1).
  • Teaching Assistant for COMP20008: Elements of Data Processing (2024 S1 / 2024 S2 / 2025 S1).