I stumbled into the world of ML/Data Science by accident, really. During my undergrad at Exeter, I was actually thinking about arranging my module choices to get me to a final year theoretical physics class, but it was a group project in one of my modules that changed this trajectory. We were tasked by a local environmental consultancy to build an automated bat monitoring device, and set ourselves a goal of including a species identification feature, for which I self-taught myself about neural networks for image classification, and I credit that experience for what really made me fall in love with the world of learning from data.
Fast forward a few years, and I’ve developed my skill set by 1) spending my MSci doing ML and Computer Vision modules (with a thesis in the application of DL and Numerical techniques to the modelling of gene expression) and 2) applying myself to as broad a range of problems as one could cast, in a variety of academic or professional settings. I’ve worked in probabilistic ML for the nuclear fusion industry, applied ML techniques to natural language tasks in the health & lifestyle industry and am currently pursuing a PhD in Medical Image Processing for Cancer Detection.
I define my academic and professional identity as a strongly interdisciplinary one, and I aim to use this website as a catalogue of my research and projects, whether that be sharing actual publications or posts about techniques and approaches in CV/ML, and how I’m using them in my work.