PhD Supervision Interests

My research interests span sequential decision making, including reinforcement learning, Bayesian optimisation, and active learning, data-driven modelling of dynamical systems, and generative modelling. I am particularly interested in developing mathematically principled methods for learning and decision making, as well as new approaches motivated by emerging real-world/AI problems (e.g., language model, RLHF, drifting model etc). I welcome enquiries from prospective students and collaborators whose interests overlap with these areas, particularly those with some prior research experience in machine learning, such as workshop papers, papers under submission, or ongoing work targeting major ML venues (ICML, NeurIPS, ICLR, etc.); those with a strong mathematical background who are interested in moving towards more data-driven research directions; those with their own research interests or preferred directions that they believe align well with my expertise; or those from other disciplines who are strongly self-motivated and confident in their ability to make a successful transition into AI research.