About

I am a computer scientist interested in signal processing and machine learning, with a particular focus on extracting structure from noisy, complex and timeseries data. I have consistently performed at a very high academic level, and I am especially motivated by research problems that demand both mathematical depth and strong computational reasoning.

What interests me most is the point at which raw observations become meaningful structure. I am drawn to problems involving signals that are incomplete, corrupted by interference or distributed across time, where useful information has to be recovered from patterns, dependencies and deviations that are not immediately visible. This naturally leads me towards representation learning, sequential modelling, uncertainty and robust inference.

More broadly, I am interested in the intersection of signal processing, machine learning, intelligent sensing and cyber-physical systems. I find research most compelling when principled signal representations and modern learning methods are used together, combining physical intuition with the flexibility of data-driven models to solve difficult real-world problems.

Research is ultimately what I want to build my career around. I will begin my PhD at University College London in October 2026, where I hope to push these interests considerably further.