About Publications Experience Teaching Service

I am a computer science Ph.D. student at Yale University advised by Professor Rex Ying. Before that, I obtained my B.S. degree at Shanghai Jiao Tong University (SJTU) in 2022. I majored in mathematics and applied mathematics (Zhiyuan Honors Program).

I am generally interested in Large Language Models (LLMs) and Graph Foundation Models (GFMs), including:

  • Developing scalable foundation models tailored for diverse graph-structured data
  • Designing efficient Graph Retireval Augmented Generation (RAG) to enhance LLM reasoning abilities
  • Applying advanced graph models to real-world applications, such as retail product networks and recommendation systems
  • Advancing LLM capabilities in temporal reasoning and time series forecasting
  • Improving AI models in terms of trustworthiness, transparency, and reliability

If you share similar interests or work in related areas, feel free to reach out. I am always open to collaboration!

Email  /  LinkedIn  /  Github  /  Google Scholar

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Selected Publications

GFSE: A Foundational Model For Graph Structural Encoding
Jialin Chen, Haolan Zuo, Haoyu Peter Wang, Siqi Miao, Pan Li, Rex Ying

Paper  /  Github

LitFM: A Retrieval Augmented Structure-aware Foundation Model For Citation Graphs
Jiasheng Zhang, Jialin Chen, Ali Maatouk, Ngoc Bui, Qianqian Xie, Leandros Tassiulas, Jie Shao, Hua Xu, Rex Ying

Paper  /  Github

From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen, Jan Eric Lenssen, Aosong Feng, Weihua Hu, Matthias Fey, Leandros Tassiulas, Jure Leskovec, Rex Ying

Paper  /  Github

DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphsy
Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, Rex Ying

Paper  /  Github

TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Jialin Chen, Rex Ying

Paper  /  Github

D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion
Jialin Chen, Shirley Wu, Abhijit Gupta, Rex Ying

Paper  /  Github

Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension
Jialin Chen, Zhuosheng Zhang, Hai Zhao

Paper  /  Github

Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen, Kenza Amara, Junchi Yu, Rex Ying
IEEE Data Engineering Bulletin, 2023

Paper  /  Github

Experience

Applied Scientist Intern
Amazon Inc., 2024.05 - Now
  • Proposed a novel graph-aware retriever to enhance LLMs’ capabilities in question answering and complex reasoning on knowledge graphs;
  • Developed joint training of graph retriever and LLM reasoner in Graph RAG pipeline, improved the model's generalizability to open-domain scenario.
  • Machine Learning Intern
    Kumo.AI Inc., 2023.05 - 2023.08
  • Worked on improving time series forecasting on retail tabular dataset;
  • Developed an autoregressive generative model for custom time series, contributing to feature development related to forecasting queries.
  • Teaching

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

    Academic Services

    • Conference Reviewer: NeurIPS, ICML, ICLR, KDD, AAAI, LoG
    • Journal Reviewer: IEEE Transactions on Artificial Intelligence, Journal of Biomedical Informatics