Ziming Liu

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Ziming Liu (刘子鸣)

AI & Physics Researcher

PhD student @ MIT & IAIFI

Email: zmliu@mit.edu

I’m on the academic job market this year, looking for faculty and postdoc positions starting 2025 fall!

Hi! I’m Ziming, a final year PhD student at MIT & IAIFI, advised by Prof. Max Tegmark. Get my CV here. My research interests lie in the intersection of AI and physics (science in general):

  • Science of AI: Understanding AI using science. I’m interested in understanding intriguing network phenomena (e.g., grokking, neural scaling laws) and their implications for next-generation AI.
  • Science for AI: Advancing AI using science. I’m obsessed with building mathematical and scientific principles into AI.
  • AI for Science: Advancing science using AI. I’m excited about inventing AI4Science models that are both accurate and interpretable.

I am grateful to the Quanta magazine (among other media outlets) which has covered all three branches of my research. Consider reading if you want to get some taste:

My research philosophy is best described by John Hopfield’s inspiring words in his Nobel lecture:

[About physics] Physics is not defined by subject matter, but is a point of view that the world around us (with effort, ingenuity and adequete resources) is understandable in a predictive and reasonably quantitative fashion.

[About choosing problems] I am now looking for a big problem whose resolution and understanding will be of significance far beyond its normal disciplinary boundaries and will reorganize the fields from which they came.

To break disciplinary boundaries, I am decicated to bring young talents into this exciting field of AI + Science, as well as bring together AI researchers and domain scientists. I’m excited to teach courses on AI + Science when opportunities arise. Take a look at the syllabus for Science of AI and AI for Science that I’m designing and let me know if you would be interested in signup if such courses exist!

latest posts

selected publications

  1. kan.png
    KAN: Kolmogorov-arnold networks
    Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y Hou, and Max Tegmark
    arXiv:2404.19756, 2024
  2. bimt.png
    Seeing is believing: Brain-inspired modular training for mechanistic interpretability
    Ziming Liu, Eric Gan, and Max Tegmark
    Entropy, 2023
  3. naturereview.png
    Scientific discovery in the age of artificial intelligence
    Nature, 2023
  4. pizza.png
    The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
    Ziqian Zhong, Ziming Liu, Max Tegmark, and Jacob Andreas
    In Thirty-seventh Conference on Neural Information Processing Systems , 2023
  5. omnigrok.png
    Omnigrok: Grokking Beyond Algorithmic Data
    Ziming Liu, Eric J Michaud, and Max Tegmark
    In The Eleventh International Conference on Learning Representations , 2023
  6. pfgm.png
    Poisson Flow Generative Models
    Yilun Xu, Ziming Liu, Max Tegmark, and Tommi S. Jaakkola
    In Advances in Neural Information Processing Systems , 2022
  7. hiddensymmetry.png
    Machine Learning Hidden Symmetries
    Ziming Liu, and Max Tegmark
    Phys. Rev. Lett., 2022
  8. poincare.png
    Machine Learning Conservation Laws from Trajectories
    Ziming Liu, and Max Tegmark
    Phys. Rev. Lett., 2021