Jiawei Liu (刘佳玮)
Avatar of Jiawei Liu
Research Keywords: Graph learning, Electronic design automation.
Email: liu_jiawei@bupt.edu.cn
Google Scholar | GitHub | Zhihu

Publications

Summary: TPAMI (1), WWW (2), IJCAI (1), DAC (1), ICCAD (2), DATE (2), etc.
  • AI-Native Electronic Design Automation:
    • Foundation Models for Circuit Learning:
      PolarGate (ICCAD'24), WideGate (DATE'25), CircuitGCL (ICCAD'25)
    • AI-Native Logical Design and Verification:
      SATGL (ISEDA'24), ForgeEDA (ISEDA'25), MILS (GLSVLSI'25, Best Paper Award Nomination).
    • AI-Native Physical Design and Verification:
      PGAU (GLSVLSI'24, First prize from the Integrated Circuit EDA Elite Challenge 2023), IR-Fusion (DATE'25), IRGNN (DAC'25).
  • Graph Machine Learning:
    • LLM-Enhanced Graph Learning / Graph Foundation Models:
      GFM-Survey (TPAMI'25), GFMRec-Survey (arXiv'25).
    • Trustworthy Graph Learning:
      DCKM (IJCAI'20), AEHCL (SDM'23), GraphPAR (WWW'24).
    • Efficient Graph Learning:
      CPF (WWW'21), LTD (WSDM'23).

    • Selected Preprints

  • Graph Foundation Models for Recommendation: A Comprehensive Survey.
    Bin Wu, Yihang Wang, Yuanhao Zeng, Jiawei Liu, Jiashu Zhao, Cheng Yang, Yawen Li, Long Xia, Dawei Yin, Chuan Shi♠.
    Preprint

    • Accepted

    Note: †Equal contribution; ♠Corresponding author

    2025

  • [C14] Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits.
    Shan Shen†, Shenglu Hua†, Jiajun Zou, Jiawei Liu, Jianwang Zhai♠, Chuan Shi, Wenjian Yu♠.
    IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2025.
  • [C13] MILS: Modality Interaction Driven Learning for Logic Synthesis.
    Mingyu Zhao, Jiawei Liu, Jianwang Zhai♠, Chuan Shi♠.
    ACM Great Lakes Symposium on VLSI (GLSVLSI), 2025. (Best Paper Award Nomination)
  • [C12] ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA.
    Zhengyuan Shi, Zeju Li, Chengyu Ma, Yunhao Zhou, Ziyang Zheng, Jiawei Liu, Hongyang Pan, Lingfeng Zhou, Kezhi Li, Jiaying Zhu, Lingwei Yan, Zhiqiang He, Chenhao Xue, Wentao Jiang, Fan Yang, Guangyu Sun, Xiaoyan Yang, Gang Chen, Chuan Shi, Zhufei Chu, Jun Yang, Qiang Xu.
    International Symposium of EDA (ISEDA), 2025.
  • [J7] Graph Foundation Models: Concepts, Opportunities and Challenges.
    Jiawei Liu†, Cheng Yang†, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi♠.
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
  • [C11] IRGNN: A Graph-based Framework Integrating Numerical Solution and Point Cloud for Static IR Drop Prediction.
    Feng Guo, Yueyue Xi, Jianwang Zhai♠, Jingyu Jia, Jiawei Liu, Kang Zhao, Chuan Shi♠.
    ACM/IEEE Design Automation Conference (DAC), 2025.
  • [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♠.
    IEEE/ACM Proceedings Design, Automation and Test in Europe (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♠.
    IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), 2025.

    • 2024

  • [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♠.
    IEEE/ACM International Conference on Computer-Aided Design (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♠.
    ACM Great Lakes Symposium on VLSI (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♠.
    International Symposium of EDA (ISEDA), 2024.
  • [C5] Endowing Pre-trained Graph Models with Provable Fairness.
    Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi♠.
    International World Wide Web Conference (WWW), 2024.
  • [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 (IJMLC), 2024.
  • [J5] Graph Foundation Model.
    Chuan Shi♠, Junze Chen, Jiawei Liu, Cheng Yang.
    Frontiers Of Computer Science (FCS), 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 (TST), 2024.

    • 2023

  • [C4] Abnormal Event Detection via Hypergraph Contrastive Learning.
    Bo Yan, Cheng Yang, Chuan Shi♠, Jiawei Liu, Xiaochen Wang.
    SIAM International Conference on Data Mining (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.
    ACM International Conference on Web Search and Data Mining (WSDM), 2023.
  • [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.

    • 2022

  • [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.

    • 2021

  • [C2] Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework.
    Cheng Yang, Jiawei Liu, Chuan Shi♠.
    International World Wide Web Conference (WWW), 2021.
  • [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.

    • 2020

  • [C1] Decorrelated Clustering with Data Selection Bias.
    Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi♠, Jiawei Liu, Bai Wang.
    International Joint Conference on Artificial Intelligence (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


  • Postdoc, The Chinese University of Hong Kong, Sep 2025 -
  • Ph.D, Computer Science and Technology, Beijing University of Posts and Telecommunications, Sep 2020 - Jun 2025
  • Visiting Student, Electronic Design Automation Center, The Chinese University of Hong Kong, Sep 2024 - Mar 2025
  • B.Eng, Computer Science and Technology (Experimental Class), Beijing University of Posts and Telecommunications, Sep 2016 - Jun 2020

    • Experiences


  • Research Assistant, Electronic Design Automation Center, The Chinese University of Hong Kong, Sep 2024 - Mar 2025
  • Research Intern, Large Circuit Model Group, National Center of Technology Innovation for EDA, Jul 2024 - Sep 2024
  • Research Intern, Supply and Operation Algorithm Group, Meituan, Sep 2021 - Nov 2022

    • Services and Talking

    Services

  • Session Chair: KDD (2025).
  • Conference Reviewer: KDD (2025, 2026).
  • Journal Reviewer: Journal of Artificial Intelligence Research (JAIR).
  • Editor: GAMMA Lab WeChat official account, 2020-2023.

    • Talks

  • An Introduction to Neural Algorithmic Reasoning, 2023.7.

    • Awards

    In the postdoctoral phase

  • To be added

    • In the doctoral phase

  • KDD2025 Outstanding Reviewer, 2025.6
  • 2024年北邮GAMMA Lab卓越贡献奖、服务优秀奖, 2025.1
  • 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

    • In the undergraduate stage

  • 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

    • AI4EDA Group

    The AI4EDA Group is co-founded by GAMMA Lab and ZhaoKang Lab.

    Graduate Students

    Hongtao Cheng BS@BUPT → MS @BUPT
    Feng Guo BS@BJUT → MS @BUPT
    Mingyu Zhao BS@BUPT → MS @BUPT
    Xun He BS@BUPT → MS @BUPT

    Selected Graduation Theses

    2022-2023 Hongtao Cheng “The Design and Implementation of SAT Solver Selection Algorithm based on Graph Neural Network”
    2023-2024 Feng Guo “The Design and Implementation of Static IR Drop Prediction Algorithm for SoC Power Networks based on Machine Learning”
    2024-2025 Mingyu Zhao “Design and Implementation of Performance Prediction Algorithm for Logic Synthesis based on Multimodal Learning”
    2024-2025 Xun He “Design and Implementation of Large-scale Circuit Representation Learning Algorithm based on Graph Machine Learning”

    Selected Projects

    2024-2025 Mingyu Zhao Beijing Natural Science Foundation Undergraduate Program“Modeling and Analysis of Large-scale Integrated Circuit Structures based on Graph Neural Networks”
    2025-2026 Shenglu Hua Beijing Natural Science Foundation Undergraduate Program“AMS Circuit Representation Learning and Parasitic Parameter Extraction based on Graph Neural Networks”