Biography

I am a Professor in University of Science and Technology of China, where I am a member of Lab of Data Science. With my colleagues, students, and collaborators, we strive to develop trustworthy deep learning and artificial intelligence algorithms with better interpretability, generalization, and robustness. Our research is motivated by, and contributes to, graph-structured applications in information retrieval (e.g., personalized recommendation), data mining (e.g., graph pre-training), security (e.g., fraud detection in fintech, information security in system), and multimedia (e.g., video question answering). Our work has over 50 publications in top-tier conferences and journals. Over 10 papers have been featured in the most cited and influential list (e.g., KDD 2019, SIGIR 2019, SIGIR 2020, SIGIR 2021) and best paper finalist (e.g., WWW 2021, CVPR 2022). Moreover, I have served as the PC member for top-tier conferences including NeurIPS, ICLR, SIGIR and KDD, and the invited reviewer for prestigious journals including JMLR, TKDE, and TOIS.

Prospective Ph.D., Master, and Undergraduate Students

I am looking for highly motivated students (PhD, master, undergraduate students) to work together on trustworthy deep learning on graph, especially pre-training, interpretability, generalization, and robustness, and their applications in real-world scenarios. Please feel free to send me your CV and transcripts, if you have interest. We are also actively looking for opportunities in research, partnership and commercialization in exciting data science projects.

News

  • [New!] 2023/01 Three papers are accepted by WWW'23! Big congrats to An Zhang, Jiancan Wu, Yuan Gao, and Other Collaborators!
         Invariant Collaborative Filtering to Popularity Distribution Shift.
         GIF: A General Graph Unlearning Strategy via Influence Function.
         Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum.

  • [New!] 2023/01 One paper is accepted by ICLR'23! Big congrats to An Zhang, and Other Collaborators!
         Boosting Causal Discovery via Adaptive Sample Reweighting.

  • [New!] 2023/01 One paper is accepted by ICSE'23! Big congrats to Jiahao Liu, and Other Collaborators!
         Learning Graph-based Code Representations for Source-level Functional Similarity Detection.

  • 2022/12 One paper is accepted by TKDE'23! Big congrats to Yinwei Wei, and Other Collaborators!
         Causal Inference for Knowledge Graph based Recommendation.

  • 2022/10 Two papers are accepted by WSDM'23! Big congrats to Yuan Gao, Junfeng Fang, and Other Collaborators!
         Alleviating Structural Distribution Shift in Graph Anomaly Detection.
         Cooperative Explanations of Graph Neural Networks.

  • 2022/09 One paper is accepted by NeurIPS'22! Big congrats to An Zhang and Other Collaborators!
         Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering.

  • 2022/09 One paper is accepted by TOIS'22! Big congrats to Hongyu Lu and Other Collaborators!
         User Perception of Recommendation Explanation: Are Your Explanations What Users Need?.

  • 2022/07 One paper is accepted by ACM MM'22! Big congrats to Yicong Li and Other Collaborators!
         Equivariant and Invariant Grounding for Video Question Answering.

  • 2022/05 One paper is accepted by ICML'22! Big congrats to Sihang Li and Other Collaborators!
         Let invariant Rationale Discovery inspire Graph Contrastive Learning.

  • 2022/05 Two papers are accepted by KDD'22! Big congrats to Yongduo Sui, Yunshan Ma, and Other Collaborators!
         Causal Attention for Interpretable and Generalizable Graph Classification.
         CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation.

  • 2022/04 One paper is accepted by TPAMI'22! Thanks all Collaborators!
         Reinforced Causal Explainer for Graph Neural Networks.

  • 2022/04 One paper is accepted by TOIS'22! Big congrats to Yuyue Zhao and Other Collaborators!
         Time-aware Path Reasoning on Knowledge Graph for Recommendation.

  • 2022/04 One paper is accepted by ISSTA'22! Big congrats to Jiahao Liu and Other Collaborators!
         TELL: Log Level Suggestions via Modeling Multi-level Code Block Information.

  • 2022/03 Two papers are accepted by CVPR'22! Big congrats to Yicong Li, Zhenguang Liu, and Other Collaborators!
         Invariant Grounding for Video Question Answering.[Oral Presentation & Best Paper Finalist]
         Temporal Feature Alignment and Mutual Information Maximization for VideoBased Human Pose Estimation.[Oral]

  • 2022/03 One paper is accepted by IEEE S&P'22! Big congrats to Jun Zeng and Other Collaborators!
         ShadeWatcher: Recommendation-guided Cyber Threat Analysis using System Audit Records.

  • 2022/01 One paper is accepted by ICLR'22! Big congrats to Yinxin Wu and Other Collaborators!
         Discovering invariant rationales for graph neural networks.

  • Highlights

  • Neural Graph Collaborative Filtering is the most cited paper in SIGIR'19. Google citations over 1000+.
  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation is the most cited paper in SIGIR'20.
  • Self-Supervised Graph Learning for Recommendation is the most cited paper in SIGIR'21.
  • Disentangled Graph Collaborative Filtering is the top-3 most cited paper in SIGIR'21.
  • KGAT: Knowledge Graph Attention Network for Recommendation is the top-2 most cited paper in KDD'19.
  • Honors and Awards

  • AI2000 Most Influential Scholars (Ranked in 13th in Information Retrieval & Recommendation), 2022
  • Best Paper Award Finalist, CVPR 2022
  • Best Paper Award Finalist, WWW 2021
  • Dean’s Graduate Research Excellence Award, National University of Singapore 2018
  • Research Achievement Award, National University of Singapore 2017
  • National Scholarship (top scholarship for Chinese undergraduates), Beihang University 2014
  • Background

  • 2021-2022: Senior Research Fellow, NExT++, National University of Singapore
         Supervisor: Prof Tat-Seng Chua
  • 2019-2021: Research Fellow, NExT++, National University of Singapore
         Supervisor: Prof Tat-Seng Chua
  • 2014-2019: PhD in Computer Science, NExT++, National University of Singapore
         Supervisor: Prof Tat-Seng Chua; Mentor: Prof Xiangnan He, Prof Liqiang Nie
  • 2010-2014: Bachelor in Computer Science, Beihang University