Biography
Jiawei Liu is a postdoctoral researcher at the Department of Computer Science and Engineering, Chinese University of Hong Kong. He received the Ph.D. degree from BUPT in 2025 and the B.Eng. degree from BUPT in 2020. His current research interests include artificial intelligence and electronic design automation. He has served as Session Chair of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) in 2025, served in the program committees of ICML, ICLR, NeurIPS, AAAI, CVPR, KDD, WWW, ECCV and CIKM.
Dr. Liu received two Best Paper Award Nomination from DATE 2026 & GLSVLSI 2025, Outstanding Reviewer Award from KDD 2025, and the 1st prize in EDA Elite Challenge 2023.
Research Interest
Electronic Automation Design (EDA):
- Circuit Modelling and Representation Learning: [ISEDA'24, GLSVLSI'24, ICCAD'24, DATE'25, DAC'25, ISEDA'25, GLSVLSI'25 BPA Nomination, ICCAD'25, ICLR'26, CVPR'26, DAC'26, TODAES'26]
- Agent for EDA: [DATE'26 BPA Nomination]
Artificial Intelligence (AI):
- Large Language Models (LLM): [TPAMI'25]
- Deep Graph Learning: [IJCAI'20, WWW'21, WSDM'23, SDM'23, WWW'24]
Publications
Note:
†Equal contribution;
♠Corresponding author
Summary
EDA Journals / Conferences: TODAES (1), DAC (2), ICCAD (2), DATE (3), etc.
AI Journals / Conferences: TPAMI (1), ICLR(1), CVPR(1), WWW (2), IJCAI (1), etc.
Presentation Locations (Sorted Alphabetically):
Journal & Conference Papers
Selected Preprints
Xinyan Zhu, Cheng Yang, Qiuyu Wang, Zeyuan Guo, Zedi Liu, Yiding Wang, Jiawei Liu, Chunchen Wang, Muhan Zhang, Chuan Shi, “Are Graphs Useful for LLMs? A Comprehensive Survey of Graph-Enhanced Large Language Models”.
Bin Wu, Yihang Wang, Yuanhao Zeng, Jiawei Liu, Jiashu Zhao, Cheng Yang, Yawen Li, Long Xia, Dawei Yin, Chuan Shi, “Graph Foundation Models for Recommendation: A Comprehensive Survey”.
Accepted
[J] Jiawei Liu, Jianwang Zhai, Xun He, Mingyu Zhao, Zhe Lin, Chuan Shi, Bei Yu, “PolarGate: Breaking the Functionality Representation Bottleneck of And-Inverter Graph Neural Network”, accepted by Transactions on Design Automation of Electronic Systems (TODAES).
2026
[C18] Feng Guo, Yueyue Xi, Jingyu Jia, Jiawei Liu, Tianshu Hou, Yuyang Ye, Jianwang Zhai, Kang Zhao, Chuan Shi, “GEMIR: Graph-Based Joint Modeling of Electromigration and IR Drop for Power Grid”, ACM/IEEE Design Automation Conference (DAC), Long Beach, Jul. 26–29, 2026.
[C17] Zewei Zhou, Jiajun Zou, Jiajia Zhang, Ao Yang, Ruichao He, Haozheng Zhou, Ao Liu, Jiawei Liu, Leilei Jin, Shan Shen, Daying Sun, “R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, June 3–7, 2026.
[C16] Mingyu Zhao, Xun He, Jiawei Liu, Jianwang Zhai, Chuan Shi, “Topology matters in RTL Circuit Representation Learning”, International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, Apr. 23–27, 2026.
[C15] Zhaohai Di, Jindong Tu, Zhiyuan HE, Yuan Pu, Jiawei Liu, Chong Tong, Tsung-Yi Ho, Bei Yu, Tinghuan Chen, “Smart-PCLib: A LLM-based Multi-Agent Framework for Automated PCB Component Library Generation”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Verona, Italy, Apr. 20–22, 2026. (Best Paper Award Nomination)
2025
[C14] Shan Shen, Shenglu Hua, Jiajun Zou, Jiawei Liu, Jianwang Zhai, Chuan Shi, Wenjian Yu, “Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Munich, Germany, Oct. 26-30, 2025.
[C13] Mingyu Zhao, Jiawei Liu, Jianwang Zhai, Chuan Shi, “MILS: Modality Interaction Driven Learning for Logic Synthesis”, ACM Great Lakes Symposium on VLSI (GLSVLSI), New Orleans, USA, Jun. 30–July 2, 2025. (Best Paper Award Nomination)
[C12] 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, “ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA”, International Symposium of Electronics Design Automation (ISEDA), Hong Kong, China, May. 9–12, 2025.
[J7] Jiawei Liu†, Cheng Yang†, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi, “Graph Foundation Models: Concepts, Opportunities and Challenges”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
[C11] Feng Guo, Yueyue Xi, Jianwang Zhai, Jingyu Jia, Jiawei Liu, Kang Zhao, Chuan Shi, “IRGNN: A Graph-based Framework Integrating Numerical Solution and Point Cloud for Static IR Drop Prediction”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 22–25, 2025.
[C10] Jiawei Liu, Zhiyan Liu, Xun He, Jianwang Zhai, Zhengyuan Shi, Qiang Xu, Bei Yu, Chuan Shi, “WideGate: Beyond Directed Acyclic Graph Learning in Subcircuit Boundary Prediction”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Lyon, France, Mar. 31–Apr. 02, 2025.
[C9] Feng Guo, Jianwang Zhai, Jingyu Jia, Jiawei Liu, Kang Zhao, Bei Yu, Chuan Shi, “IR-Fusion: A Fusion Framework for Static IR Drop Analysis Combining Mathematical Solutions and Machine Learning”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Lyon, France, Mar. 31–Apr. 02, 2025.
[J6] Jiawei Liu, Wenyi Xiao, Hongtao Cheng, Chuan Shi, “Graph Neural Network based Time Estimator for SAT Solver”, International Journal of Machine Learning and Cybernetics (IJMLC), 2025.
2024
[C8] Jiawei Liu, Jianwang Zhai, Mingyu Zhao, Zhe Lin, Bei Yu, Chuan Shi, “PolarGate: Breaking the Functionality Representation Bottleneck of And-Inverter Graph Neural Network”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.
[C7] Feng Guo†, Jiawei Liu†, Jianwang Zhai, Jingyu Jia, Kang Zhao, Chuan Shi, “PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing”, ACM Great Lakes Symposium on VLSI (GLSVLSI), Tampa Bay Area, FL, June 12–14, 2024.
[C6] Hongtao Cheng†, Jiawei Liu†, Jianwang Zhai, Mingyu Zhao, Cheng Yang, Chuan Shi, “SATGL: an Open-source Graph Learning Toolkit for Boolean Satisfiability”, International Symposium of Electronics Design Automation (ISEDA), Xi'an, China, May. 10-13, 2024.
[C5] Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi, “Endowing Pre-trained Graph Models with Provable Fairness”, ACM The Web Conference (WWW), Singapore, May 13–17, 2024.
[J5] Chuan Shi, Junze Chen, Jiawei Liu, Cheng Yang, “Graph Foundation Model”, Frontiers Of Computer Science (FCS), 2024.
[J4] Jiawei Liu, Haihan Gao, Cheng Yang, Chuan Shi, Tianchi Yang, Hongtao Cheng, Qianlong Xie, Xingxing Wang, Dong Wang, “Heterogeneous Spatio-temporal Graph Contrastive Learning for Point-of-Interest Recommendation”, Tsinghua Science and Technology (TST), 2024.
2023 and before
[C4] Bo Yan, Cheng Yang, Chuan Shi, Jiawei Liu, Xiaochen Wang, “Abnormal Event Detection via Hypergraph Contrastive Learning”, SIAM International Conference on Data Mining (SDM), Minneapolis-St. Paul Twin Cities, MN, USA, Apr. 27–29, 2023.
[C3] Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin, “Learning to Distill Graph Neural Networks”, ACM International Conference on Web Search and Data Mining (WSDM), Singapore, Feb. 27–Mar. 03, 2023.
[J3] Jiawei Liu, Haihan Gao, Chuan Shi, Hongtao Cheng, Qianlong Xie, “Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation”, Applied Sciences, 2023.
[J2] Jiawei Liu, Chuan Shi, Cheng Yang, Zhiyuan Lu, Philip S.Yu, “A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources”, AI Open, 2022.
[C2] Cheng Yang, Jiawei Liu, Chuan Shi, “Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework”, International World Wide Web Conference (WWW), Ljubljana, Slovenia, Apr. 19–23, 2021.
[J1] Jiawei Liu, Chuan Shi, Cheng Yang, Philip S.Yu, “Heterogeneous information network based recommender systems: A survey”, Journal of Information Security (In Chinese), 2021.
[C1] Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang, “Decorrelated Clustering with Data Selection Bias”, International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, Jul. 11–17, 2020.
Books / Book Chapters
[B1] Jiawei Liu, “Distilling Graph Neural Networks”, In: Advances in Graph Neural Networks, Springer, 2022.
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
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
Program Committee Member: ICML, ICLR, NeurIPS, AAAI, CVPR, KDD, WWW, ECCV, CIKM.
Journal Reviewer: Transactions on Design Automation of Electronic Systems (TODAES), Transactions on Reconfigurable Technology and Systems (TRETS), Journal of Artificial Intelligence Research (JAIR).
Selected Awards
Best Paper Award Nomination, DATE, 2026.
Best Paper Award Nomination, GLSVLSI, 2025.
Outstanding Reviewer, KDD, 2025.
1st Prize, EDA Elite Challenge, 2023.
Outstanding Graduate of Beijing, 2020.
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