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Cai Zhou 周偲

1st-year PhD @ MIT EECS.
Massachusetts Institute of Technology
caiz428@mit.edu


About Me

I am a first year PhD student in MIT EECS, co-advised by Tommi Jaakkola and Stephen Bates. I am affiliated with CSAIL and LIDS. My recent research focuses on the intersection of machine learning, generative models and AI4Science.

Prior to my PhD, I received a bachelor from the Department of Automation, School of Information at Tsinghua University. I am also a member of Class of General Artificial Intelligence of Tsinghua University, and I minor in statistics. In my undergraduate research, I am fortunate to work with Muhan Zhang at Institute for Artificial Intelligence of Peking University. We have great collaborations working on machine learning theories, graph neural networks, AI4Science and LLMs/foundation models. In the summer of 2023, I had the privilege to intern at UCSD advised by Yusu Wang and Rose Yu on machine learning. I also worked with Gao Huang at Tsinghua University on computer vision.

Research Interests

My research interest lies broadly in theoretical and applied machine learning. I aim to understand the foundations of machine learning, with a special focus on its probabilistic and geometric nature. I’m also interested in application areas including computer vision, natural language processing and computational biology. Some of my interested research topics include:

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Publications & Preprints

  1. Preprint
    Geometric Representation Condition Improves Equivariant Molecule Generation
    Zian Li, Cai Zhou, Xiyuan Wang, Xingang Peng, Muhan Zhang
    Preprint.
    PDF

  2. Preprint
    Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
    Junru Zhou, Cai Zhou, Xiyuan Wang, Pan Li, Muhan Zhang
    Preprint.
    PDF

  3. Preprint
    FiTv2：Scalable and Improved Flexible Vision Transformer for Diffusion Model
    Zidong Wang, Zeyu Lu, Di Huang, Cai Zhou, Wanli Ouyang, Lei Bai
    Preprint.
    PDF Code

  4. AAAI 2025
    ChemVLM：Exploring the Power of Multimodal Large Language Models in Chemistry Area
    ..., Cai Zhou, ...
    Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2025.
    PDF

  5. NeurIPS 2024
    Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
    Cai Zhou, Xiyuan Wang, Muhan Zhang
    Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS), 2024.
    PDF Code BibTex

  6. IJCV 2024
    Locally Supervised Deep Learning by Maximizing Information Propagation
    Yulin Wang, Zanlin Ni, Yifan Pu, Cai Zhou, Jixuan Ying, Shiji Song, Gao Huang
    International Journal of Computer Vision (IJCV), 2024.
    PDF

  7. AISTATS 2024
    On the Theoretical Expressive Power and Design Space of High Order Graph Transformers
    Cai Zhou, Rose Yu, Yusu Wang
    Twenty-seventh International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.
    PDF Code BibTex

  8. NeurIPS 2023
    Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
    Cai Zhou, Xiyuan Wang, Muhan Zhang
    Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.
    PDF Code BibTex

  9. ICML 2023
    From Relational Pooling to Subgraph GNNs：A Universal Framework for More Expressive Graph Neural Networks
    Cai Zhou*, Xiyuan Wang*, Muhan Zhang
    Fortieth International Conference on Machine Learning (ICML), 2023.
    PDF Code BibTex

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