Here you will find several blog posts I have written over the last few years. If you spot any mistakes or have any questions drop me an email!
Here you will find some supporting material for my most recent submission to ICRA 2022. More will be added over the next few months. Watch this space...
Here you will find a landing page supporting the project "There and Back Again: Learning to Simulate Radar For Real World Applications" presented at the International Conference on Robotics and Automation (ICRA) 2021.
During the first lockdown, with more free time on my hands, I began to think a little more philosophically about the foundations of mathematics. In particular how can we know for sure that maths is correct? I discovered that at the start of the 20th century this was a question that an enormous amount of time and effort was devoted to trying to answer. Of the solutions proposed, the work of Zermelo and Fraenkel alongside the axiom of choice, commonly abbreviated to ZFC, remains one of the most complete answers to date. In this post I begin by looking at why ZFC is necessary, what it is, how it might be used to ground many of the important foundations of mathematics today.
Since their conception in 2014, the use of generative adversarial networks has exploded throughout machine learning, vision and robotics. Alongside novel application domains, much time and effort has been devoted to developing new architectures and training approaches. Modern instantiations of the GAN training paradigm are responsible for some truly remarkable results. However, in trying to access the wealth of material that is out there, the sheer quantity can be difficult to penetrate. This blog is intended to be a brief introduction to generative adversarial networks and their development over the last few years. It is by no means exhaustive but simply communicates my ideas of what GANs are and my interpretation of how we got here. So without further a do...
Here you will find the slides I presented at several reading groups designed to be a general intro to Bayesian Inference and linear regression. Much of the content is based on the Murpy and Bishop machine learning books which I thoroughly recommend if you want to find out more. If you have any questions or want to discuss anything please drop me an email.
This page contains a cheat sheet covering much of the first year electrical engineering course at the University of Oxford. It grew out of a collection of notes I made for myself whilst teaching undergraduate tutorials over the last couple of years and I hope it will be a useful resource for others. We begin by considering Maxwells equations which - remarkably - are able to describe the entireity of classical electromagentism and optics and underpin the workings of all the electrical devices covered below. Next , linear circuit devices are introduced, including resistors , capacitors and inductors, and a selection of methods for analysing both AC and DC circuits are summarised. If you have any questions / spot any mistakes please do drop me an email.