Yu He (Heyu)

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Hi, I am Heyu, a second-year CS PhD student at Stanford, where I am fortunate to be advised by Ellen Vitercik. Previously, I graduated from University of Cambridge with BA+MEng in Computer Science, where I was supervised by Pietro Liò. My research interests lie in graph neural networks, geometric deep learning, and their applications in combinatorial optimization.

publications

  1. primal-dual.png
    Primal-Dual Neural Algorithmic Reasoning
    Yu He and Ellen Vitercik
    In International Conference on Machine Learning (ICML), 2025
    Spotlight (top 2.6%)
  2. dem.png
    Deep Equilibrium Models For Algorithmic Reasoning
    Sophie Xhonneux, Yu He, Andreea Deac, Jian Tang, Gauthier Gidel
    In The Third Blogpost Track at International Conference on Learning Representations (ICLR), 2024
    Paper Blog
  3. higher-egp.png
    Higher-Order Expander Graph Propagation
    Thomas Christie* and Yu He*
    In NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023
    Paper Code Poster
  4. sheaf-pe.png
    Sheaf-based Positional Encodings for Graph Neural Networks
    Yu He, Cristian Bodnar, and Pietro Liò
    In NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations, 2023
    Paper Poster
  5. cnap.png
    Continuous Neural Algorithmic Planners
    Yu He, Petar Veličković, Pietro Liò, Andreea Deac
    In Proceedings of the First Learning on Graphs Conference, 2022
    Paper Code Poster
  6. algo-selection.png
    Algorithm Selection for Classification Problems via Cluster-based Meta-features
    Daren Ler, Hongyu Teng, Yu He, Rahul Gidijala
    In 2018 IEEE International Conference on Big Data (Big Data), 2018
    Paper