Max W.F. Ku

University of Waterloo; Vector Institute;

I am a first-year PhD student in Computer Science at the Faculty of Mathematics, University of Waterloo, 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 focused on advancing controllable generation and editing of visual media (image, video, and beyond), and on evaluating and improving the interpretability of generative models.

At the core of my research is a simple but ambitious goal:

Make visuals fully controllable and extend this control beyond visuals.

My work covers:

  • Generative AI
  • Controllable Generation / Editing (Image, Video, and more)
  • Multi Modalities (Visuals + X)
  • Interpretability (by all means)
  • Creative applications in Entertainment and Education

news

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!
Sep 26, 2024 GenAIArena got accepted to NeurIPS 2024! See you in Vancouver!
May 15, 2024 VIEScore got accepted to ACL 2024 Main! See you in Bangkok!

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
    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
    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 Twelfth International Conference on Learning Representations , 2024