Jiaqi Han

PhD student at Stanford CS.


I am currently a first year PhD student at Stanford Computer Science, advised by Prof. Stefano Ermon. Previously, I obtained B.S. in Computer Science at Tsinghua University, where I was very fortunate to work with Prof. Wenbing Huang during my undergrad.

My core research interests lie in generative models, dynamics simulation, and graph neural networks. I am particularly interested in geometrically equivariant GNNs which are powerful tools for learning interactions in complicated physical systems in a highly data-efficient fashion.

Welcome to drop me an email if you want to discuss or collaborate!


Nov 20, 2022 Our team received the first place of NeurIPS’22 Open Catalyst Challenge, and was invited for the winner’s talk at the event!
Sep 25, 2022 Two papers on equivariant GNNs for science (EGHN, SGNN) have been accepted by NeurIPS 2022.
Jan 30, 2022 Equivariant Graph Mechanics Networks has been accepted by ICLR 2022.

selected publications [full list]

(*) denotes equal contribution

  1. AAAI
    Energy-motivated equivariant pretraining for 3d molecular graphs
    Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2023
  2. EGHN.png
    Equivariant graph hierarchy-based neural networks
    Jiaqi Han, Wenbing Huang, Tingyang Xu, and Yu Rong
    Advances in Neural Information Processing Systems, 2022
  3. SGNN.gif
    Learning physical dynamics with subequivariant graph neural networks
    Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li, Josh Tenenbaum, and Chuang Gan
    Advances in Neural Information Processing Systems, 2022
  4. ICLR
    Equivariant Graph Mechanics Networks with Constraints
    Wenbing Huang*, Jiaqi Han*, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang
    In International Conference on Learning Representations, 2022
  5. KDD
    Multivariate Time Series Anomaly Detection and Interpretation Using Hierarchical Inter-Metric and Temporal Embedding
    Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, and Dan Pei
    In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021