Jialin Chen

I am Jialin Chen, a second-year computer science Ph.D. student at Yale University advised by Professor Rex Ying.

I am generally interested in deep learning on graph-structured data, including:

graph representation learning and efficient pretraining on graphs,
explainability and trustworthiness of graph models,
graph learning applications

Previously, I obtained my B.S. degree at Shanghai Jiao Tong University (SJTU) in 2022. I majored in mathematics and applied mathematics (Zhiyuan Honors Program)

Email / LinkedIn / Github /

News

I am actively looking for research intern opportunities for summer 2024.

Research (* denotes equal contribution)

	TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery Jialin Chen, Rex Ying NeurIPS, 2023 paper / code
	D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion Jialin Chen, Shirley Wu, Abhijit Gupta, Rex Ying NeurIPS, 2023 paper / code
	Generative Explanations for Graph Neural Network: Methods and Evaluations Jialin Chen, Kenza Amara, Junchi Yu, Rex Ying IEEE Data Engineering Bulletin, 2023 paper / code
	Approximate Equivariance SO(3) Needlet Convolution Kai Yi, Jialin Chen, Yu Guang Wang, Bingxin Zhou, Pietro Liò, Yanan Fan, Jan Hamann ICML Workshop on Topology, Algebra, and Geometry in Machine Learning, 2022 paper / code
	Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension Jialin Chen, Zhuosheng Zhang, Hai Zhao COLING, 2022 paper / code

Teaching

Teaching Assistant, 2023 Fall, Deep Learning on Graph-Structured Data (CPSC 483) Course Website
Teaching Assistant, 2023 Spring, Trustworthy Deep Learning (CPSC 680)