Rate My Professor Wenqi Fan

Fan's research interests center on machine learning, data mining, and artificial intelligence, particularly recommender systems, graph neural networks, large language models, retrieval-augmented generation, management information systems, and trustworthy AI encompassing adversarial attacks, robustness, fairness, and explainability. He has authored numerous publications in top-tier venues including ACM WWW, KDD, SIGIR, NeurIPS, ICDE, IJCAI, and IEEE TKDE. Key works include "Graph Neural Networks for Social Recommendation" (WWW 2019), "A Graph Neural Network Framework for Social Recommendations" (IEEE TKDE 2020), "Graph Trend Filtering Networks for Recommendation" (SIGIR 2022), "Fairness Reprogramming" (NeurIPS 2022), "Knowledge-enhanced Black-box Attacks for Recommendations" (KDD 2022), and "A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models" (KDD 2024). His impactful contributions have earned him the AI 2000 Most Influential Scholar Honorable Mention in 2022, 2023, 2024, and 2025; recognition as one of Stanford University's World's Top 2% Scientists in 2023, 2024, and 2025; the Early Career Researcher Individual Award for Outstanding Achievement in Research and Scholarly Activities in 2025; and student travel awards from SDM 2020 and IJCAI 2019. Fan has delivered tutorials such as "Trustworthy Recommender Systems: Foundations and Frontiers" at KDD 2023 and IJCAI 2023, and "Recommender Systems in the Era of Large Language Models" at ICDM 2023 and IJCAI 2024. He serves as a program committee member for conferences like ICML, WWW, AAAI, KDD, and IJCAI, and reviews for journals including TKDE, TOIS, and TIST. His research is funded by the Hong Kong Research Grants Council, National Natural Science Foundation of China, and Hong Kong Innovation and Technology Commission.