I am a Ph.D. candidate in Engineering at the University of Sydney, specializing in multimodal learning. I hold a Bachelor of Advanced Computing (Honours) in Computer Science and Computational Data Science from the University of Sydney (GPA 3.91/4.00, First-Class Honours, top 1%) and am supported by an Australian Government RTP Scholarship. During my undergraduate studies, I received the Ian Jackson Memorial Prize, was named a Dalyell Scholar, and consistently appeared on the Dean’s List.
I worked as a machine learning engineer for TikTok, where I developed the first audio-visual-language model for general content understanding and downstream content moderation. I now joined joined The University of Sydney as an Associate Lecturer for COMP5328: Advanced Machine Learning. My research on multimodal learning mainly focuses on:
(1) Multimodal foundation models for general understanding;
(2) Content creation;
(3) Evidence-based Reasoning.
🔥 News
- 2026.09: A co-authored paper on medical vision-language model is accepted to NeurIPS 2026
- 2026.09: A paper on audio-visual-language foundation model is accepted to NeurIPS 2026
- 2026.07: I joined The University of Sydney as an Associate Lecturer for Unit COMP5328: Advanced Machine Learning.
- 2026.05: A co-authored paper on multimodal prognosis prediction is accepted to MICCAI 2026
- 2026.01: I joined TikTok as a machine learning engineer to develop the first audio-visual-language model for general content understanding and downstream content moderation
- 2025.10: A paper on medical report generation is accepted to TMM
- 2025.03: A paper on textual reliance in language-guided segmentation is accepted to ICCV 2025
- 2024.10: A paper on modality preference bias in medical VQA is accepted to the workshop in ACM MM 2024
- 2024.05: A paper on medical language-guided segmentation is accepted to MICCAI 2024
- 2024.03: I commenced my PhD in Engineering at the University of Sydney, focusing on multimodal learning and funded by an Australian Government RTP Scholarship.
- 2023.12: I graduated from the University of Sydney with a Bachelor of Advanced Computing (First Class Honours; WAM 95+)
- 2021.01: I was invited to be a Dalyell Scholar.
- 2022.09: I joined The University of Sydney as a research assistant.
- 2022.02: I participate in ICPC on behalf of The University of Sydney
📝 Publications
TikTok Research: Shuchang Ye, Jinqiang Yu, Zhujun Xiao, Yajing Kong, Yist Y. Lin, Yang Ma, Jiaxi Liu, Xiaolei Xu, Zheng Yu
Dynamic Traceback Learning for Medical Report Generation
Shuchang Ye, Mingyuan Meng, Mingjian Li, Dagan Feng, Usman Naseem, Jinman Kim
Shuchang Ye, Usman Naseem, Mingyuan Meng, Jinman Kim
A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering
Shuchang Ye, Usman Naseem, Mingyuan Meng, Dagan Feng, Jinman Kim
Enabling Text-Free Inference in Language-Guided Segmentation of Chest X-Rays via Self-guidance
Shuchang Ye, Mingyuan Meng, Mingjian Li, Dagan Feng, Jinman Kim
🎖 Honors and Awards
- 2024.03 Australia Government RTP Scholarship.
- 2023.09 Dean’s List excellence in academic performance
- 2023.07 The Ian Jackson Memorial Prize for Computer Science
- 2021.01 Dalyell Scholar
📖 Educations
- 2024.03 - Present, Doctor of Philosophy (Engineering), The University of Sydney
- Thesis: Multimodal Learning
- 2020.03 – 2024.03: Bachelor of Advanced Computing (Honours), The University of Sydney
- Major: Computer Science, Computational Data Science
💻 Work Experience
- 2026.01 - Present, Machine Learning Engineer, TikTok (ByteDance), Sydney, Australia
- 0->1 development of TikTok’s first Audio-Visual-Language foundation model (AVLM) for video and live moderation: data pipeline, pre-training, post-training, and downstream supervised fine-tuning and alignment.
- Deployed to production moderation systems, significantly reducing Community Guidelines Violation Rate (CGVR) and Creator Overkill Rate (COR) compared to existing ASR+VLM approach.
- 2026.07 - Present, Associate Lecturer, The University of Sydney, Sydney, Australia
- COMP5328: Advanced Machine Learning (This course introduces some fundamental machine learning concepts, learning problems and algorithms)
📜 Reviewer Certificates
- 2025, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Certificate