😊 About me
Hello! I am Cuiying Huo, an Assistant Professor at Tianjin University. In January 2025, I obtained my Ph.D. degree from Tianjin University, supervised by Prof. Di Jin (金弟) and Prof. Dongxiao He (何东晓). Since then, I have been conducting postdoctoral research under the supervision of Prof. Wenhuan Lu (路文焕), with close academic guidance from Prof. Jianguo Wei (魏建国). From October 2023 to October 2024, I was a visiting Ph.D. student at Nanyang Technological University (NTU), supervised by Prof. Gao Cong (丛高). My research interests include graph machine learning, graph neural networks, spatiotemporal data mining, and their applications.
霍翠英，天津大学软件学院助理研究员，硕士生导师。长期从事图数据挖掘、可信人工智能、大语言模型等方面的研究。主持国家自然科学基金青年项目，中国博士后科学基金面上项目、国家资助博士后研究人员计划C档项目等。在本领域顶级期刊/会议上发表论文20篇，获得CCF A类会议WWW-2021最佳论文亚军奖（获奖率1‰）、天津大学优秀博士学位论文、天津大学科技创新先进个人等荣誉。长期担任TPAMI、ICML、ICLR、NeurIPS、KDD、WWW、AAAI等CCF A类期刊/会议审稿人，现任中国中文信息学会社会媒体处理专业委员会委员。
招收2026、2027级硕士研究生，有意者请将个人简历发送至邮箱：huocuiying@tju.edu.cn，欢迎联系交流！
🥇 Honors and Awards
- The Postdoctoral Fellowship Program of CPSF (C), 2025
- Excellent PhD Thesis of Tianjin University, 2025
- The Web Conference 2021 (CCF A类会议), Best Paper Award Runner-Up, 2021
📚 Services
- Conference PC member: NeurIPS, WWW, ICLR, ICML, MM, KDD, AAAI, IJCAI, …
- Journal reviewer: TPAMI, TNNLS, TOIS, TDSC, TKDD, TWEB, TNSE, TBD, Pattern Recognition, …
📝 Publications
ICDE 2027Robust Traffic Flow Forecasting under Anomalies via Decoupled Spatio-Temporal Expert Collaboration, Di Jin, Kaile Zhang, Cuiying Huo#(Corresponding Author), Lin Wu, Wenhuan Lu, Dongxiao He. (CCF A类会议长文)NeurIPS 2026Learning Continuously Evolving Spatio-Temporal Explanations for Traffic Flow Forecasting, Cuiying Huo, Baoxu Wang, Lin Wu, Yu Mei, Dongxiao He, Yawen Li, Di Jin. (CCF A类会议长文)IJCAI 2026Multi-Semantic Aware Self-Supervised Learning for Multi-Label Node Classification, Jiayu Zhang, Jitao Zhao, Dongxiao He, Cuiying Huo, Zhiyong Feng. (CCF B类会议长文)WWW 2026IVQ-GNN: Mitigating Performance Gap from Graph Connection Pattern Inconsistency via Vector Quantization, Di Jin, Yixuan Du, Cuiying Huo#(Corresponding Author), Xiaotong Huang, Ruqiong Zhang, Xiaobao Wang, Yawen Li. (CCF A类会议长文)AAAI 2026DuoKD: Dual Knowledge Distillation from Large Language Models for Robust Graph Neural Networks, Cuiying Huo, Xiaotong Huang, Dongxiao He, Yixuan Du, Wenhuan Lu, Di Jin. (CCF A类会议长文, Oral)TKDE 2025Distill & Contrast: A New Graph Self-Supervised Method with Approximating Nature Data Relationships, Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Cuiying Huo, Di Jin, Witold Pedrycz, Weixiong Zhang. (CCF A类期刊长文)WWW 2025LLGformer: Learnable Long-range Graph Transformer for Traffic Flow Prediction, Di Jin, Cuiying Huo#(Corresponding Author), Jiayi Shi, Dongxiao He#,Jianguo Wei, Philip S. Yu. (CCF A类会议长文)AAAI 2025Feature-Structure Adaptive Completion Graph Neural Network for Cold-start Recommendation, Songyuan Lei, Xinglong Chang, Zhizhi Yu, Dongxiao He, Cuiying Huo, Jianrong Wang, Di Jin. (CCF A类会议长文)TIST 2024Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning, Cuiying Huo, Dongxiao He, Yawen Li, Di Jin, Jianwu Dang, Witold Pedrycz, Lingfei Wu, Weixiong Zhang. (JCR Q1)TOIS 2024PEPT: Expert Finding Meets Personalized Pre-training, Qiyao Peng, Hongyan Xu, Yinghui Wang, Hongtao Liu, Cuiying Huo, Wenjun Wang. (CCF A类期刊)IJCAI 2024Graph collaborative expert finding with contrastive learning, Qiyao Peng, Wenjun Wang, Hongtao Liu, Cuiying Huo, Minglai Shao. (CCF A类会议长文)AAAI 2024A New Mechanism for Eliminating Implicit Conflict in Graph Contrastive Learning, Dongxiao He, Jitao Zhao, Cuiying Huo#(Corresponding Author), Yongqi Huang, Yuxiao Huang, Zhiyong Feng#. (CCF A类会议长文)World Wide Web 2024Bridging Distribution Gaps: Invariant Pattern Discovery for Dynamic Graph Learning, Yucheng Jin, Maoyi Wang, Yun Xiong, Zhizhou Ren, Cuiying Huo, Feng Zhu, Jiawei Zhang, Guangzhong Wang, Haoran Chen. (CCF B类期刊)TNNLS 2023TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature, Cuiying Huo, Dongxiao He, Chundong Liang, Di Jin, Tie Qiu, Lingfei Wu. (JCR Q1)AAAI 2023T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation, Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang, Lingfei Wu. (CCF A类会议长文, Oral)WWW 2023KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks, Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu. (CCF A类会议长文)KSEM 2023Local-Global Fusion Augmented Graph Contrastive Learning Based on Generative Models, Di Jin, Zhiqiang Wang, Cuiying Huo, Zhizhi Yu, Dongxiao He, Yuxiao Huang. (CCF C类会议长文)TNNLS 2022Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning, Dongxiao He, Chundong Liang, Cuiying Huo, Zhiyong Feng, Di Jin, Liang Yang, Weixiong Zhang. (JCR Q1)WWW 2021Heterogeneous Graph Neural Network via Attribute Completion, Di Jin, Cuiying Huo, Chundong Liang, Liang Yang. (CCF A类会议长文, Best Paper Award Runner-Up)NeurIPS 2021Universal Graph Convolutional Networks, Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, Jiawei Han. (CCF A 类会议长文)