Wenqi Fan

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Dr. Wenqi Fan is currently a Research Assistant Professor (RAP) of the Department of Computing (COMP), The Hong Kong Polytechnic University (PolyU). Before joining the Department, he worked as a Postdoctoral Fellow at PolyU. He received his Ph.D. degree in computer science from the City University of Hong Kong (CityU) in 2020, under the supervision of Prof. Qing LI and Prof. Jianping WANG. From 2018 to 2021, he was a research scholar of Data Science and Engineering (DSE) Lab at Michigan State University (MSU), under the supervision of Prof. Jiliang Tang. He is recognized as the 2022 AI 2000 Most Influential Scholars Honorable Mention (“被提名为2022年AI 2000人工智能最具影响力学者!” ). His research has been supported by multiple government research fund agencies, including National Natural Science Foundation of China (NSFC), Hong Kong Research Grants Council (RGC-GRF), and Hong Kong Innovation and Technology Commission (ITF), etc. He has published his research in highly ranked journals and top conference proceedings [Google Scholar].

Email: wenqifan03(at)gmail(dot)com, wenqifan(at)polyu(dot)edu(dot)hk

Office: PQ724c, Mong Man Wai Building, Hung Hom, Kowloon, Hong Kong SAR.

>>>Openings: I am actively recruiting self-motivated Postdoc, Ph.D. students, Joint Ph.D. students (i.e., UTS、CAS、SUSTech, etc.), MPhil/Msc, and Research Assistants, etc. Visiting scholars, interns, and self-funded students are also welcome. Send me an email if you are interested. [Please see here for position details.]

>>> Welcome to contact me if you are interested in interdisciplinary research (collaborating with our group) using Data Mining/Machine Learning/Artificial Intelligence!

Research Interests [Google Scholar]

Data Mining and Machine Learning, with a particular focus on:

  • Graph Neural Networks (GNNs)

  • Recommender Systems (RecSys)

  • Adversarial Attacks and Defenses



  • 05/2022: One paper (Knowledge-enhanced Black-box Attacks for Recommendations) got accepted by KDD’2022. Congratulations to my incoming PhD student Jingfan for his first paper with PolyU.

  • 05/2022: Our survey paper (Trustworthy AI: A Computational Perspective [Arxiv]) is accepted by ACM TIST. In the survey, we examine SIX dimensions in achieving trustworthy AI, including (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being, and review the latest research works in each dimension from a computational perspective.

  • 04/2022: Our new preprint “Automated Machine Learning for Deep Recommender Systems: A Survey” is online.

  • 04/2022: 1 paper (Graph Trend Filtering Networks for Recommendation [Arxiv] [Code-Pytorch]) got accepted by SIGIR’2022.

  • 03/2022: I am invited to serve as a reviewer for NeurIPS 2022.

  • 03/2022: I was recognized as “2022年AI 2000人工智能最具影响力学者提名奖!” (“2022 AI 2000 Most Influential Scholar Honorable Mention” in Information Retrieval and Recommendation) based on the Tsinghua-AMiner academic data. 榜单链接Link.


  • 12/2021: Our tutorial on “Trustworthy AI: A Computational Perspective” is accepted for is accepted for the Web Conference (WWW’22) [Website].

  • 12/2021: Our tutorial on “Automated Machine Learning for Recommendations: Fundamentals and Advances” is accepted for the Web Conference (WWW’22) [Website].

  • 12/2021: One paper got accepted by AAAI’22.

  • 11/2021: Invited to serve as the Senior PC Member of KDD’22.

  • 09/2021: 1 paper got accepted by ICDM’2021.

  • 07/2021: Our new preprint “Trustworthy AI: A Computational Perspective” is online.

  • 07/2021: Our tutorial on “Trustworthy AI: A Computational Perspective” is accepted for ICAPS 2021.

  • 06/2021: Invited to serve as PC Member for ICLR 2020 and WSDM 2022.

  • 06/2021: A Chinese Survey on Graph Nerual Networks based Recommender Systems, “综述:基于图学习的推荐系统”.

  • 05/2021: Our paper “AutoLoss: Automated Loss Function Search in Recommendations” is accepted by KDD 2021.

  • 04/2021: Invited to serve as PC member for NeurIPS/CIKM 2021.

  • 04/2021: Our tutorial on “Deep Learning for Recommendations: Fundamentals and Advances” is accepted to be held in IJCAI 2021.


  • 12/2020: Our tutorial “Deep Recommender System: Fundamentals and Advances” has been accepted by WWW 2021.

  • 12/2020: Invited to serve as PC member for ICML 2021.

  • 11/2020: Invited to serve as PC member for KDD 2021.

  • 10/2020: One full paper got accepted by ICDE 2021.

  • 09/2020: One paper got accepted by COLING 2020 about Fairness/bias in Dialogue Systems.

  • 08/2020: Invited to serve as PC member for AAAI2021 and IJCAI2021.

  • 06/2020: One paper “A Graph Neural Network Framework for Social Recommendations” is accepted by IEEE TKDE.

  • 05/2020: Preprint “Attacking Black-box Recommendations via Copying Cross-domain User Profiles[Arxiv]

  • 03/2020: SDM2020 Doctoral Forum accepted “Deep Social Recommendations”.

  • 03/2020: Awarded SDM2020 Student Travel Award with funds from SIAM.


  • 12/2019: Two papers are accepted by SDM 2020.

  • 12/2019: Invited to serve as PC member for IJCAI 2020 and AI4EDU 2020.

  • 10/2019: Preprint of our paper “Does Gender Matter? Towards Fairness in Dialogue Systems” is released [link].

  • 10/2019: Our paper “Epidemic Graph Convolutional Network” is accepted by WSDM 2020 (Preprint coming soon!).

  • 06/2019: Our paper “Deep Social Collaborative Filtering” is accepted by RecSys 2019.

  • 06/2019: Received the IJCAI 2019 Student Travel Award and will serve as volunteer.

  • 05/2019: Our paper “Deep Adversarial Social Recommendation” is accepted by IJCAI 2019.

  • 01/2019: Our paper “Graph Neural Networks for Social Recmmendation” is accepted by WWW 2019.