My research involves the application of novel analysis methods to astronomical data. The main focus is time series analysis of light curves from accreting compact objects (black holes and neutron stars), where the goal is to understand gravity in the strong field regime, extreme physics and the role these objects play in shaping the universe. In particular, I use X-ray reverberation mapping and quasi-periodic oscillations (QPOs) to study matter spiralling in the inner accretion flow around a black hole. I am currently working on neural network methods to find important signals in the presence of red noise.

I also work on interdisciplinary machine learning projects, including climate science, economics and cryptography.

Personal webpages: https://coherentnoise.space

I am the module leader for the Data Science courses on Advanced Research Topics and Research Methods. In these modules, the students are exposed to cutting-edge data science methods and there application in a research setting as well as their use in industry. I also supervise several Masters reserach projects.