Rate My Professor Xuhui Fan

Fan’s research focuses on generative AI, large language models, Gaussian processes, deep generative models, variational inference methods, stochastic point processes, Bayesian nonparametrics, space partitions, and Markov chain Monte Carlo methods. He has authored numerous publications in leading journals and conferences, contributing significantly to advancements in machine learning and artificial intelligence. Key works include 'Data-dependent Rectangular Bounding Processes' (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026), co-authored with Bin Li, Prosha A. Rahman, and Scott A. Sisson; 'Federated Neural Nonparametric Point Processes' (Artificial Intelligence, 2026), with Hui Chen and others; 'Hyperbolic-Enhanced Mixture-of-Experts Mamba for Sequential Recommendation' (AAAI Conference on Artificial Intelligence, 2026); 'PBDD: a prompt-based learning approach for few-shot social media depression detection' (IEEE Transactions on Computational Social Systems, 2026); and 'A client–server based recognition system: non-contact single/multiple emotional and behavioral state assessment methods' (Computer Methods and Programs in Biomedicine, 2025). Earlier notable publications encompass 'Free-Form Variational Inference for Gaussian Process State-Space Models' (ICML, 2023), 'Continuous-Time Edge Modelling using Non-Parametric Point Processes' (NeurIPS, 2021), 'Rectangular Bounding Process' (NeurIPS, 2018), and several others presented at ICML, AISTATS, and NeurIPS. Fan leads the Macquarie Generative AI research group and serves as Chief Investigator on the project 'LP23: Ethical Enterprise Representations for Personalised Sustainable Finance' (2025–2028). He is affiliated with the Data Horizons Research Centre and Frontier AI Research Centre at Macquarie University.