Publications

Research Interest

Learning for EDA:

• Signal Probability Analysis: PolarGate (ICCAD'24), ForgeEDA (ISEDA'25)
• Functional Boundary Identification: WideGate (DATE'25)
• Parasitic Extraction: CircuitGCL (ICCAD'25)
• SAT-based Verification: SATGL (ISEDA'24)
• Logic Synthesis: MILS (GLSVLSI'25, Best Paper Award Nomination)
• IR Drop Analysis: PGAU (GLSVLSI'24), IR-Fusion (DATE'25), IRGNN (DAC'25)
• PCB Design: Smart-PCLib (DATE'26)

Learning on Graphs:

• Graph Foundation Models: GFM-Survey (TPAMI'25), GFMRec-Survey (arXiv'25)
• Trustworthy Graph Learning: DCKM (IJCAI'20), AEHCL (SDM'23), GraphPAR (TheWebConf'24)
• Efficient Graph Learning: CPF (TheWebConf'21), LTD (WSDM'23)

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Accepted

Note: ^†Equal contribution; ^♠Corresponding author

Summary

EDA Journals / Conferences: DAC (1), ICCAD (2), DATE (3), etc.

AI Journals / Conferences: TPAMI (1), TheWebConf (2), IJCAI (1), etc.

2026

[C15] Smart-PCLib: A LLM-based Multi-Agent Framework for Automated PCB Component Library Generation.
Zhaohai Di, Jindong Tu, Zhiyuan HE, Yuan Pu, Jiawei Liu, Chong Tong, Tsung-Yi Ho, Bei Yu, Tinghuan Chen.
IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), 2026.

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 (TheWebConf), 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 (TheWebConf), 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.

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

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

Postdoctoral Fellow, the Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sep 2025 - Present

Ph.D, Computer Science and Technology, Beijing University of Posts and Telecommunications, Sep 2020 - Jun 2025

Visiting Student, the Department of Computer Science and Engineering, 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

Selected Awards

Best Paper Award Nomination (6 out of 154), GLSVLSI 2025, 2025.6

Outstanding Reviewer, KDD 2025, 2025.6

First Prize, EDA Elite Challenge, 2023.12

Outstanding Graduate of Beijing, 2020.6

National Second Prize, CUMCM, 2018.10

Services and Talking

Services

Program Committee Member & Conference Reviewer: ICLR (2026), AAAI (2026), CVPR (2026), KDD (2025, 2026), TheWebConf (2026)

Journal Reviewer: Transactions on Design Automation of Electronic Systems (TODAES), Journal of Artificial Intelligence Research (JAIR).

Session Chair: KDD (2025).

Editor: GAMMA Lab WeChat Official Account (2020-2023).

Talks

An Introduction to Neural Algorithmic Reasoning, 2023.7.