Rate My Professor Qian Li

Dr. Qian Li's research specializations include causal reasoning for machine learning, topological data analysis, Riemannian optimization, optimal transport, computer vision, data mining, natural language processing, and data science, with applications in medical science, insurance, energy, and commerce. She has authored over 50 articles in high-quality venues such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, ACM Transactions on Recommender Systems, ACM Transactions on Knowledge Discovery from Data, VLDB Journal, Expert Systems with Applications, Neurocomputing, and Pattern Recognition, as well as conferences including CIKM, SIGIR, WWW, CVPR, and AAAI (CORE A*/A). Key publications are "Causality-guided Graph Learning for Session-based Recommendation" (CIKM 2023), "Counterfactual Explainable Conversational Recommendation" (IEEE TKDE 2023), "Be Causal: De-biasing Social Network Confounding in Recommendation" (ACM TKDD 2022), "Deep Treatment-Adaptive Network for Causal Inference" (VLDB 2021), and "Hilbert Sinkhorn Divergence for Optimal Transport" (CVPR 2021). Her contributions have advanced causal inference, fairness-aware recommendations, and counterfactual explanations in AI. Dr. Li received awards including the 2016 Chinese National Graduate Scholarship (Top 1%), 2015 Director Scholarship (Top 1%), and 2010 Luxembourg Student Scholarship (Top 2%). She served on the Program Committee for AAAI 2022.