I am a machine learning scientist at Apple
I have research interests in applied machine learning, Bayesian inference and computational neuroscience
I have 14+ years of R&D experience spanning academia and industry, and a background in computer science. I specialise in translating the latest machine learning and statistical inference methods into scalable real-world applications in industry and academia.
I have worked on a range of a wide range of machine learning domains, including unsupervised, supervised and reinforcement learning, time series analysis, probabilistic inference and network modelling.
I have co-authored 50+ peer-reviewed publications in academic journals and conferences, with a h-index of 26 (as of 2022). My research career spans academia (Universities of Kent and Cambridge) and industry (Apple, Oracle). I have also worked as a consultant to multiple technology startups, and as a software engineer.
My research has been featured by the BBC, Wired Magazine, New Scientist and Discovery Channel.
Some highlights from my research over the years
A documentary about our research into the mohawk of consciousness.
A podcast by the Naked Scientists about our work on measuring consciousness during anaesthesia.
A 2016 BBC Radio 4 documentary about our research into bedside assessments of brain injury patients, following our 2014 paper into EEG brain connectomics for measuring consciousness.
The EPSRC funded our project to develop software for EEG brain connectomics. The MOHAWK MATLAB app we developed is free to download on GitHub.
I wrote a blog post about my 2013 internship at Prof. Adrian Owen's group in Canada. Thanks to the UK Foreign and Commonwealth Office for supporting this work.
PDF of my doctoral thesis on The Temporal Spotlight of Attention.