Hi there, I am Jiacheng (James) Zhang. I am a research intern in the TikTok Data Trust & Safety team, mentored by Dr Haoyu He, 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, under the supervision of Dr. Feng Liu and Prof. Ben Rubinstein. Previously, I was an associate member of Sea AI Lab (SAIL), mentored by Dr. Tianyu Pang and Dr. Chao Du. I received my Honours degree with a University Medal (Top 1 in my major) from the University of Sydney, where I was fortunate to learn from A/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 AI agents; (2) safety alignment for large vision-language models; (3) adversarial robustness 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 Dr. Feng Liu and Prof. Ben Rubinstein.
- 2022.07 - 2023.07, the University of Sydney (USYD), B.Sc. in Data Science (Honours), advised by A/Prof. Tongliang Liu.
- 2019.03 - 2022.03, the University of Melbourne (Unimelb), B.Sc. in Data Science.
📝 Selected Publications
* Co-first author, ✉️ Corresponding author.
🎖 Honours and Awards
- 2025.07, Optiver PhD Quant Lab Travel Scholarship.
- 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, Mathematics and Statistics Vacation Research Scholarship of Unimelb.
💻 Internships
- 2026.02 - Now, research intern @TikTok - Data Trust & Safety, mentored by Dr. Haoyu He.
- 2025.07 - 2026.01, research intern @Sea AI Lab - Trustworthy AI, mentored by Dr. Tianyu Pang and Dr. Chao Du.
✍️ Academic Services
- Conference Reviewer for ICML, NeurIPS, ICLR, CVPR, AISTATS, etc.
- Journal Reviewer for IEEE TPAMI, IEEE TIFS, TMLR, Machine Learning, NEUNET, etc.
💬 Invited Talks
- 2025.12, Invited Talk on “How to Improve Model Robustness? A Distributional-Discrepancy Perspective” @Unimelb NLP Group Seminar, Melbourne. [slides]
- 2025.11, Invited Talk on “How to Improve Model Robustness? A Distributional-Discrepancy Perspective” @TMLR Seminar, Melbourne. [slides]
- 2025.08, Invited Talk on “Leveraging Distributional Discrepancies for Accuracy-robustness Trade-off” @AI TIME, Online. [slides]
- 2025.06, Invited Talk on “Leveraging Distributional Discrepancies for Accuracy-robustness Trade-off” @AI TIME, Online. [slides] [video]
- 2025.01, Invited Talk on “Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training” @Unimelb, Melbourne. [slides]
- 2024.11, Invited Talk on “Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training” @AJCAI 2024, Melbourne. [slides]
🧑🏫 Teaching Assistants
- Teaching Assistant for SWEN20003: Object Oriented Software Development (2024 S2 / 2025 S1 / 2025 S2).
- Teaching Assistant for COMP20008: Elements of Data Processing (2024 S1 / 2024 S2 / 2025 S1 / 2025 S2).