Max W.F. Ku

University of Waterloo; Vector Institute;

I’m a first-year PhD student in Computer Science at the University of Waterloo, Faculty of Mathematics, where I’m fortunate to be advised by Prof. Wenhu Chen.

My research lies at the intersection of generative AI, visual content creation, and model interpretability. I’m particularly interested in making visual generation and editing (images, videos, and beyond) more controllable, interpretable, and usable in creative applications.

At the heart of my work is a simple but ambitious goal:

Make visuals fully controllable, across whatever applications I care about.

My work covers:

  • Generative AI
  • Controllable Editing and Generation (Image, Video, and more)
  • Multimodal Agentic Systems (Visuals + X)
  • Interpretability (e.g. Explanation)
  • Creative AI Applications in Entertainment and Education

news

Jun 15, 2025 Achieved a total of 1000 citations.
Jun 11, 2025 DisProtEdit got accepted to 2025 ICML GenBio workshop and FM4LS workshop.
Jun 02, 2025 Joined NVIDIA Deep Imagination Research as an intern for Summer 2025.
May 15, 2025 TheoremExplainAgent got accepted to ACL 2025 Main!
Nov 03, 2024 AnyV2V got accepted to TMLR 2024!

latest posts

selected publications

  1. ACL 2025
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    TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding
    Max Ku*, Thomas Chong*, Jonathan Leung, Krish Shah, Alvin Yu, and 1 more author
    In The 63rd Annual Meeting of the Association for Computational Linguistics , 2025
  2. NeurIPS 2024
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    GenAI Arena: An Open Evaluation Platform for Generative Models
    Dongfu Jiang*, Max Ku*, Tianle Li*, Yuansheng Ni, Shizhuo Sun, and 2 more authors
    In The Conference on Neural Information Processing Systems , 2024
  3. TMLR 2024
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    AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
    Max Ku*, Cong Wei*, Weiming Ren*, Harry Yang, and Wenhu Chen
    Transactions on Machine Learning Research, 2024
  4. ACL 2024
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    VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
    Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue, and Wenhu Chen
    In The 62nd Annual Meeting of the Association for Computational Linguistics , 2024
  5. ICLR 2024
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    ImagenHub: Standardizing the evaluation of conditional image generation models
    Max Ku, Tianle Li, Kai Zhang, Yujie Lu, Xingyu Fu, and 2 more authors
    In The 12th International Conference on Learning Representations , 2024