I’ve always been driven by a passion for innovation and discovery, and my work has inevitably been shaped by this — and my insatiable curiosity. I’ve been fortunate to have been involved with and contributed to some of the most interesting (for me at least!) research and commercial ventures of today.
I have experience working with and implementing a wide range of machine learning technologies including Bayesian Networks (e.g. Naive Bayes, discrete Dynamic Bayesian Nets, Kalman Filters, & HMMs), Markov Networks (CRFs, MRFs), Neural Networks (e.g. feedforward, autoencoders, echo state networks), SVMs and various assorted unsupervised and statistical modelling techniques. I have an extensive academic background in mathematically demanding fields.
I’m also an experienced programmer, with experience developing software in a research context in Python and C++. I am comfortable with other languages including C and MATLAB. I’m particularly experienced in scientific programming tasks such as data analysis, data visualisation and process simulation. I’m also comfortable with the usual web development languages (the usual HTML, CSS, JS), as well as the Django Python web framework and data visualisation libraries including D3.js.
University of York
I have studied for both my Masters and PhD at The University of York. As a member of both the Physics and Computer Science departments I have been exposed to a range of cutting-edge technologies.
I work at Peak AI in Manchester (UK) as a member of AI Engineering group. I'm helping to develop an enterprise Artificial Intelligence System (AIS) for business.
My PhD research was performed with the support and direct input of BAE Systems. This research involved the development of strategies for the assurance of mission-critical Artificial Intelligence.
Republic of Things
The Republic of Things (RoT) develops technologies for the Internet of Things. I have supported the development and deployment of multiple products as a technical consultant for RoT.
Last CV update on: 01/01/2018