I am a final year Ph.D student in Computer Science Department at UIUC under supervision of Prof. Jiawei Han. I received my Bachelor and Master degree in Computer Science at Zhejiang University and University of Illinois, respectively.

I am motivated to design graph learning algorithms that are robust to in-the-Wild distribution shifts in real-world applications. Specifically, I work on:

• Principles: transferable pre-training of GNNs (EGI), handling localized training data (SR-GNNs), domain adaptation via optimal Transport (GDOT)
• Applications: entity alignment on unknown types(CG-Align), name disambiguation in academia network (GAND)and etc.

Here is my resume. Free free to drop me an email for dicsussion (qiz3 AT illinois.edu).

Talks

Overcoming the Limitations of Localized Graph Training data

@ Graph Intelligence Sciences team at Microsoft MSAI, May 2022 Slides

Designing Robust Graph Neural Network against Distribution Shift

@ DGL Team, May 2022 Slides

Selected Publications

(*) indicates equal contributions

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data Paper Code

Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

Proc. 2021 Conf. on Neural Information Processing Systems (NeurIPS’21)

Transfer learning of graph neural networks with ego-graph information maximization Paper

Qi Zhu^*, Carl Yang^*, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han

Proc. 2021 Conf. on Neural Information Processing Systems (NeurIPS’21)

Collective Multi-type Entity Alignment Between Knowledge Graphs Paper Code Slides Media Coverage

Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han

International World Wide Web Conference(WWW), 2020

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks Paper Code

Yu Shi^*, Qi Zhu^*, Fang Guo, Chao Zhang, Jiawei Han (^* equal contribution)

International Conference on Knowledge Discovery & Data Mining(KDD), 2018