My research focusses on the development of statistically grounded methods for the detection of anomalies in streaming data. Using tools that can be connected to statistical models which explain the processes we observe allow us to explain why data has been flagged as anomalous, and understand the inner workings of our method. 

I am currently working on the AI4ME grant, and so my research is currently focussed on modelling and monitoring multimedia streams to assure the quality of user experience. Related to this, I am involved in several projects related to the detection of anomalies in computer networks.

Outside of this, from a mathematical research perspective I am also interested in nonparametric statistics, and applications of functional data.

My previous teaching experience includes tutoring undergraduates and postgraduates on a range of mathematical topics, from calculus and linear algebra, to probability and statistics.

I have also been involved in the teaching R and Python to undergraduate math students, and MSc Data Science Students.