Shurui Gui

Ph.D. Student, Texas A&M University

shurui.gui [AT] tamu.edu

Bio

Howdy! I am currently a 4th year Ph.D. student at Texas A&M University, advised by Dr. Shuiwang Ji, the leader of the Data Integration, Visualization, and Exploration (DIVE) Lab. I got my B.E. from University of Science and Technology of China in Computer Science and Technology (honor class) in 2020.

My research interests are general machine learning and deep learning. My interest topics include Graph learning, Out-of-Distribution generalization, Causality, and Explainability. Specifically, I am exploring how causal inference can guide us with more generalizable machine learning model in different tasks, e.g., chemistry, bioinfomation, and physical simulations. One significant challenge is how can we verify the validity of our causal assumptions.

I am enthusiastically open to collaboration and actively seeking internship opportunities. If you're interested in working together or have opportunities available, please feel free to reach out to me!

News

Publications [Google Scholar]

* indicates equal contribution.

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

arXiv preprint arXiv:2307.08423

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization

Xiner Li, Shurui Gui, Youzhi Luo, Shuiwang Ji

arXiv preprint arXiv:2306.08076

2024

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

Shurui Gui*, Xiner Li*, Shuiwang Ji

The Twelfth International Conference on Learning Representations (ICLR), 2024

2023

FlowX: Towards Explainable Graph Neural Networks via Message Flows

Shurui Gui, Hao Yuan, Jie Wang, Qicheng Lao, Kang Li, Shuiwang Ji

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji

Neural Information Processing Systems (NeurIPS), 2023

2022

GOOD: A Graph Out-of-Distribution Benchmark

Shurui Gui*, Xiner Li*, Limei Wang, Shuiwang Ji

Neural Information Processing Systems (NeurIPS), 2022
Datasets and Benchmarks Track

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

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

2020

Featureflow: Robust video interpolation via structure-to-texture generation

Shurui Gui*, Chaoyue Wang*, Qihua Chen, Dacheng Tao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020

Services

Program Committee Member & Reviewer

NeurIPS on Datasets and Benchmarks track 2022, 2023

Annual Conference on Neural Information Processing Systems (NeurIPS) 2023

International Conference on Machine Learning (ICML) 2023

International Conference on Learning Representation (ICLR) 2023, 2024

Learning on Graphs Conference (LoG) 2022, 2023

International Conference on Information and Knowledge Management (CIKM) 2023

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

Résumé

  • Texas A&M University 2020 - now (Pandemic Gap Year: 2020 fall - 2021 summer)
    Ph.D. Student
    DIVE Lab, Advisor: Dr. Shuiwang Ji
  • West China Hospital of Sichuan University 2020 - 2021
    Intern
    Research assistant, Mentor: Dr. Kang Li
  • The University of Sydney Summer 2019
    Intern
    Research Scientist, Mentor: Dr. Dacheng Tao
  • USTC 2016 - 2020
    B.E. Student
    Computer Science and Technology (Honor class)