Cai Zhou 周偲 | Massachusetts Institute of Technology

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:

Theoretical Machine Learning: optimization, statistics, game theory
Generative Models: diffusion models, LLMs, multi-modal foundation models
AI for Science: geometric deep learning, AI4biology, AI4healthcare

News

[Jun. 2024] Graduated from Tsinghua University with distinct honor: awarded Outstanding Graduate by Beijing Ministry of Education and by Tsinghua University; also awarded Outstanding Undergraduate Thesis by Beijing Ministry of Education and by Tsinghua University.
[Jan. 2024] Our paper on higher-order transformers is accepted to AISTATS 2024.
[Dec. 2023] I’m attending NeurIPS in New Orleans to present my paper. Welcome chat!
[Oct. 2023] Awarded the National Scholarship (Top 0.5% in Tsinghua University).
[Sept. 2023] Our paper Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes is accepted to NeurIPS 2023.
[May. 2023] Our paper From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks is accepted to ICML 2023.

Publications & Preprints

Preprint

Latent Graph Diffusion：A Unified Framework for Generation and Prediction on Graphs

Cai Zhou, Xiyuan Wang, Muhan Zhang

Preprint. Under review.

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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.

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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.

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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.

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Preprint

Locally Supervised Deep Learning by Maximizing Information Propagation

Yulin Wang, Zanlin Ni, Yifan Pu, Cai Zhou, Shiji Song, Gao Huang

International Journal of Computer Vision (IJCV). Under review.

Services

Conference Reviewers

Artificial Intelligence and Statistics (AISTATS) 2024