Research. My main current research interests are in statistical modelling of dynamic network graphs, Gaussian process modelling, changepoint analysis and anomaly detection, with application areas including enterprise cyber-security.
Grant funding. My research activity in modelling dynamic networks is supported by an EPSRC programme grant on Network Stochastic Processes and Time Series (NeST), which is a large collaboration between Imperial, Bristol, Oxford, Bath, LSE, York. I co-lead the NeST project on Dynamic graph embeddings: procedures and inference.
Some recent publications:
PhD opportunities. I am always interested to hear from potential PhD students. Please feel free to get in touch by email to discuss potential research projects before applying.
Teaching. I teach MATH70100 Bayesian Methods and Computation, which is a compulsory module on the MSc Machine Learning and Data Science (MLDS) degree programme at Imperial. The lecture notes are taken from my textbook, An Introduction to Bayesian Inference, Methods and Computation. I am also the programme director of MSc MLDS.