Research Interests. I develop graph machine learning (GML) techniques and its applications on electronic design automation (EDA) tasks, with a focus on but not limited to graph neural networks (GNN4EDA), graph foundation models (GFM4EDA), neuro-symbolic GML4EDA and trustworthy GML4EDA.

1. Accelerate EDA tasks by leveraging GML techniques.
2. Develop open-source GML tools to support applications in EDA.
3. Develop neuro-symbolic and trustworthy AI-native EDA techinques via GML techniques.
4. Develop large circuit models (LCMs).

Research Keywords: Graph machine learning, AI for EDA.

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Recent Publications

See my Google Scholar for the full publication list. ^†Equal contribution; ^♠Corresponding author

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Preprints & Working Papers

• Towards Graph Foundation Models: A Survey and Beyond.
  Jiawei Liu^†, Cheng Yang^†, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi^♠.
  Preprint

Journal Papers

• [J6] Graph Neural Network based Time Estimator for SAT Solver.
  Jiawei Liu, Wenyi Xiao, Hongtao Cheng, Chuan Shi^♠.
  International Journal of Machine Learning and Cybernetics, 2024.
• [J5] Graph Foundation Model.
  Chuan Shi^♠, Junze Chen, Jiawei Liu, Cheng Yang.
  Frontiers Of Computer Science, 2024.
• [J4] Heterogeneous Spatio-temporal Graph Contrastive Learning for Point-of-Interest Recommendation.
  Jiawei Liu, Haihan Gao, Cheng Yang, Chuan Shi^♠, Tianchi Yang, Hongtao Cheng, Qianlong Xie, Xingxing Wang, Dong Wang.
  Tsinghua Science and Technology, 2024.
• [J3] Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation.
  Jiawei Liu, Haihan Gao, Chuan Shi^♠, Hongtao Cheng, Qianlong Xie.
  Applied Sciences, 2023.
• [J2] A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources.
  Jiawei Liu, Chuan Shi^♠, Cheng Yang, Zhiyuan Lu, Philip S.Yu.
  AI Open, 2022.
• [J1] 基于异质信息网络的推荐系统研究综述.
  刘佳玮, 石川^♠, 杨成, 菲利普·俞.
  信息安全学报, 2021.

Conference Papers

• [C10] WideGate: Beyond Directed Acyclic Graph Learning in Subcircuit Boundary Prediction.
  Jiawei Liu, Zhiyan Liu, Xun He, Jianwang Zhai^♠, Zhengyuan Shi, Qiang Xu, Bei Yu, Chuan Shi^♠.
  DATE, 2025.
• [C9] IR-Fusion: A Fusion Framework for Static IR Drop Analysis Combining Mathematical Solutions and Machine Learning.
  Feng Guo, Jianwang Zhai^♠, Jingyu Jia, Jiawei Liu, Kang Zhao, Bei Yu, Chuan Shi^♠.
  DATE, 2025.
• [C8] PolarGate: Breaking the Functionality Representation Bottleneck of And-Inverter Graph Neural Network.
  Jiawei Liu, Jianwang Zhai^♠, Mingyu Zhao, Zhe Lin, Bei Yu, Chuan Shi^♠.
  ICCAD, 2024.
• [C7] PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing.
  Feng Guo^†, Jiawei Liu^†, Jianwang Zhai^♠, Jingyu Jia, Kang Zhao, Chuan Shi^♠.
  GLSVLSI, 2024.
• [C6] SATGL: an Open-source Graph Learning Toolkit for Boolean Satisfiability.
  Hongtao Cheng^†, Jiawei Liu^†, Jianwang Zhai, Mingyu Zhao, Cheng Yang, Chuan Shi^♠.
  ISEDA, 2024.
• [C5] Endowing Pre-trained Graph Models with Provable Fairness.
  Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi^♠.
  WWW, 2024.
• [C4] Abnormal Event Detection via Hypergraph Contrastive Learning.
  Bo Yan, Cheng Yang, Chuan Shi^♠, Jiawei Liu, Xiaochen Wang.
  SDM, 2023.
• [C3] Learning to Distill Graph Neural Networks.
  Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi^♠, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin.
  WSDM, 2023.
• [C2] Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework.
  Cheng Yang, Jiawei Liu, Chuan Shi^♠.
  WWW, 2021.
• [C1] Decorrelated Clustering with Data Selection Bias.
  Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi^♠, Jiawei Liu, Bai Wang.
  IJCAI, 2020.

Book Chapters

• Chapter 7: Distilling Graph Neural Networks.
  In: Advances in Graph Neural Networks. Springer. 978-3-031-16173-5. 2022.
  Preprint
• Chapter 3: Structure-preserved Heterogeneous Graph Representation.
  In: Heterogeneous Graph Representation Learning and Applications. Springer. 978-981-16-6166-2. 2022.
  Preprint

Education

- Ph.D. Computer Science and Technology, Beijing University of Posts and Telecommunications, Sep 2020 - present

- B.Eng. (Experimental Class) Computer Science and Technology, Beijing University of Posts and Telecommunications, Sep 2016 - June 2020

Experiences

- Research Assistant, Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sep 2024 - Present

- Research Intern, Large Circuit Model Group, National Center of Technology Innovation for EDA, July 2024 - Sep 2024

- Research Intern, Supply and Operation Algorithm Group, Meituan, Sep 2021 - Nov 2022

Awards

- 2024年中国研究生创“芯”大赛·EDA精英挑战赛总决赛学术进取奖, 2024.12

- 2023年北邮GAMMA Lab服务卓越奖、项目优秀奖、学术优秀奖, 2024.1

- 第五届集成电路EDA设计精英挑战赛总决赛一等奖、行芯企业特别奖, 2023.12

- 2022年北邮GAMMA Lab项目卓越奖、服务优秀奖、学术贡献奖, 2023.1

- 北京邮电大学计算机学院优秀研究生, 2021.12

- 2021年北邮GAMMA Lab服务优秀奖、学术优秀奖, 2021.12

- 北京邮电大学计算机学院"助顺邮我"科技扶贫团队重要贡献个人, 2021.3

- 2020年北邮GAMMA Lab服务优秀奖、学术贡献奖, 2020.12

- 北京市优秀毕业生、北京邮电大学优秀毕业生, 2020.6

- 2019年北邮DMGroup服务贡献奖、学术贡献奖, 2020.1

- 2018-2019学年北京邮电大学校级三好学生、优秀团员, 2019.12

- 美国大学生数学建模竞赛H奖, 2019.4

- 2019年北京邮电大学寒假大学生回母校宣传活动优秀个人, 2019.4

- 2017-2018学年北京邮电大学校级三好学生、优秀团员, 2018.12

- 全国大学生数学建模竞赛全国二等奖, 2018.10

- 2018高校校园大数据竞赛优胜奖, 2018.6

- 北京邮电大学程序设计竞赛银奖, 2018.4

- 2016-2017学年北京邮电大学校级三好学生、优秀团员, 2017.12

- 河北省大学生程序设计竞赛一等奖, 2017.10