About Me
Hello! My name is Gang Liu. I am a first-year Ph.D. student in Computer Science and Engineering at the University of Notre Dame, advised by Dr. Meng Jiang. I am glad to be a member of the Data Mining towards Decision Making (DM2) Lab, which is directed by Dr. Meng Jiang.
I received my Bachelor degree in Software Engineering at Southwest University, China, in 2021. When I was an undergraduate, I was luckily advised by Prof. Yong Deng and Prof. Fuyuan Xiao.
For my education and research experience, please refer to my Experience.
Research Interest
My current research interests are machine learning and data mining on graphs, with a particular focus on graph-level applications such as polymer and molecule property predictions. Besides, I am always interested in the topics of interpretability, generalization, and robustness in machine learning. Welcome to contact me for potential discussions and collaborations if you find we have any common interests.
What’s New
| May 2022 | One paper “Graph Rationalization with Environment-based Augmentations” accepted by KDD 2022. |
| One paper “Learning from Counterfactual Links for Link Prediction” accepted by ICML 2022. Congratulations to Dr. Tong Zhao! | |
| Glad to launch my personal website! |
Publication
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Please find all my publications on Google Scholar.
- Preprint
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Graph Data Augmentation for Graph Machine Learning: A Survey
[Paper] [Reading List]
Tong Zhao, Gang Liu, Stephan Günnemann, and Meng Jiang.
arXiv:2202.08871, 2022
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- Conference
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Graph Rationalization with Environment-based Augmentations [Paper] [Code]
Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2022 -
Learning from Counterfactual Links for Link Prediction [Paper] [Code]
Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang.
International Conference on Machine Learning, 2022
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Service
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Conference Reviewer: ICML 2022; NeurIPS 2022.
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Journal Reviewer: IEEE Transactions on Cybernetics; Digital Signal Processing.
Templated adapted from Ankit Sultana and Otilia Stretcu. Thanks for their great work!