Haiyang Yu
Ph.D. Student, Texas A&M University
haiyang [AT] tamu.edu
Bio
Howdy! I am currently a 5th-year Ph.D. student in the Department of Computer Science & Engineering, Texas A&M University. My advisor is Prof. Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory . I obtained my bachelor’s degree from the School of Information Science and Technology, University of Science and Technology of China (USTC) in 2020. Here is my CV.
My research interests are deep learning and machine learning. Specifically, I am currently working on (1) graph deep learning, (2) AI for science, and (3) trustworthy AI. Currently, my publication is related to the explainability on Graph Neural Networks and training GNN on large-scale graphs. I build a benchmark with seven GNN explainability methods over five datasets in our DIG xgraph benchmark . In addition, I create a Github repo for the collection of papers on large-scale graph.
News
Publications [Google Scholar]
* indicates equal contribution, and ** denotes equal senior contribution.2025
Learning to Discover Regulatory Elements for Gene Expression Prediction
Xingyu Su*, Haiyang Yu*, Degui Zhi, Shuiwang Ji
International Conference on Learning Representations, 2025
2024
Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction
Zhao Xu*, Haiyang Yu*, Montgomery Bohde, Shuiwang Ji
Transactions on Machine Learning Research (TMLR Featured), 2024
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Meng-Chieh Lee, Haiyang Yu, Jian Zhang, Vassilis N. Ioannidis, Xiang song, Soji Adeshina, Da Zheng, Christos Faloutsos
International Conference on Learning Representations (ICLR Spotlight), 2024
Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks
Meng Liu, Haiyang Yu, and Shuiwang Ji
Transactions on Machine Learning Research (TMLR), 2024
2023
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
Haiyang Yu, Zhao Xu, Xiaofeng Qian**, Xiaoning Qian**, Shuiwang Ji**
International Conference on Machine Learning (ICML), 2023
2022
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
Frontiers of Graph Neural Networks with DIG
Shuiwang Ji*, Meng Liu*, Yi Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Zhao Xu*, Haiyang Yu*
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022
Hands-on Tutorial
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Haiyang Yu*, Limei Wang*, Bokun Wang*, Meng Liu, Shuiwang Ji
International Conference on Machine Learning (ICML), 2022
2021
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji
Journal of Machine Learning Research (JMLR), 2021
On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji
International Conference on Machine Learning (ICML), 2021
Preprint
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
Services
Program Committee Member & Reviewer [Selected]
International Conference on Machine Learning (ICML) 2021, 2022
International Conference on Learning Representation (ICLR) 2023
Learning on Graphs Conference (LoG) 2022
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Vitæ
Here is my CV[PDF].
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Amazon Web Service June 2023 - Aug 2023
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Texas A&M University Aug 2020 - now
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University of Sydney July 2019 - Sept 2019
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USTC Aug 2016 - Jul 2020B.E. Student
Electronic Engineering and Information Science, University of Science and Technology of China