On Explainability of Graph Neural Networks via Subgraph Explorations. - Hao Yuan 001; Haiyang Yu 005; Jie Wang 005; Kang Li 004; Shuiwang Ji (ICML, 2021)

DIG: A Turnkey Library for Diving into Graph Deep Learning Research. - Meng Liu 015; Youzhi Luo; Limei Wang; Yaochen Xie; Hao Yuan 001; Shurui Gui; Haiyang Yu 005; Zhao Xu 005; Jingtun Zhang; Yi Liu 059; Keqiang Yan; Haoran Liu; et al. (J. Mach. Learn. Res., 2021)

GraphFM: Improving Large-Scale GNN Training via Feature Momentum. - Haiyang Yu 005; Limei Wang; Bokun Wang; Meng Liu 015; Tianbao Yang; Shuiwang Ji (ICML, 2022)

Frontiers of Graph Neural Networks with DIG. - Shuiwang Ji; Meng Liu 015; Yi Liu 059; Youzhi Luo; Limei Wang; Yaochen Xie; Zhao Xu 005; Haiyang Yu 005 (KDD, 2022)

Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks. - Meng Liu 015; Haiyang Yu 005; Shuiwang Ji (CoRR, 2022)

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian. - Haiyang Yu 005; Zhao Xu 005; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (ICML, 2023)

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules. - Haiyang Yu 005; Meng Liu 015; Youzhi Luo; Alex Strasser; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (NeurIPS, 2023)

Explainability in Graph Neural Networks: A Taxonomic Survey. - Hao Yuan 001; Haiyang Yu 005; Shurui Gui; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils. - Jacob Helwig; Xuan Zhang; Haiyang Yu 005; Shuiwang Ji (CoRR, 2024)

Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction. - Zhao Xu 005; Haiyang Yu 005; Montgomery Bohde; Shuiwang Ji (Trans. Mach. Learn. Res., 2024)

Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm. - Meng Liu 015; Haiyang Yu 005; Shuiwang Ji (Trans. Mach. Learn. Res., 2024)

Learning to Discover Regulatory Elements for Gene Expression Prediction. - Xingyu Su; Haiyang Yu 005; Degui Zhi; Shuiwang Ji (ICLR, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling. - Jacob Helwig; Sai Sreeharsha Adavi; Xuan Zhang; Yuchao Lin; Felix S. Chim; Luke Takeshi Vizzini; Haiyang Yu 005; Muhammad Hasnain; Saykat Kumar Biswas; John J. Holloway; Narendra Singh; N. K. Anand; et al. (CoRR, 2025)

NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis. - Mouadh Yagoubi; David Danan; Milad Leyli-Abadi; Jocelyn Ahmed Mazari; Jean-Patrick Brunet; Abbas Kabalan; Fabien Casenave; Yuxin Ma; Giovanni Catalani; Jean Fesquet; Jacob Helwig; Xuan Zhang; et al. (CoRR, 2025)

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames. - Haiyang Yu 005; Yuchao Lin; Xuan Zhang; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials. - Cong Fu 003; Yuchao Lin; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations. - Yuchao Lin; Cong Fu 003; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)