I am a PhD student in the Doctoral Training Centre in Neuroinformatics, part of the School of Informatics at University of Edinburgh. My supervisor is Amos Storkey and I am part of his machine learning research group.
My research is focused on developing methods for performing efficient approximate inference in complex probabilistic models.
My PhD project has been primarily concerned with developments to Markov Chain Monte Carlo (MCMC) methods. These are a group of techniques for performing approximate inference with probabilistic models.
The core idea of MCMC methods is to simulate a stochastic dynamical system where the probability distribution over the state of the system converges to the distribution of interest. The samples of the system state can then be used to estimate expectations (averages under a probability distribution) of the model. How well the dynamic is able to explore the state-space determines how quickly these estimates converge to the correct values.
I am specifically considering methods which augment the system state space with additional auxiliary variables. In some cases this allows the robustness or efficiency of sampling methods to be improved, for example by making it easier for the sampler to coherently explore the target distribution or to increase movement between modes in multimodal target distributions. In other settings redefining the state space of the problem can allow us to perform inference in settings where we do not have an explicit form for the distribution on the variables of interest.
Matthew M. Graham and Amos J. Storkey
To appear in: Electronic Journal of Statistics
Matthew M. Graham and Amos J. Storkey
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence
Matthew M. Graham and Amos J. Storkey
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
Iain Murray and Matthew M. Graham
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics
Matthew M. Graham and Amos J. Storkey
ICML 2017 workshop: Implicit generative models
Matthew M. Graham and Amos J. Storkey
NIPS 2016 workshop: Advances in Approximate Bayesian Inference
Seminar at School of Mathematics and Statistics, University of Newcastle
20th International Conference on Artificial Intelligence and Statistics
BIRS workshop: Validating and Expanding Approximate Bayesian Computation Methods
NIPS 2016 workshop: Advances in Approximate Bayesian Inference
This project was motivated by the work of Kathrin Steck and colleagues who discovered that Cataglyphis fortis, a Saharan desert ant species, are able to use odour sources in their environment as 'landmarks' to help when navigating back to their nest. A field study was conducted attempting to see if the previous results could be observed when ants were subjected to a more complex navigation task and an information-theoretic analysis used to try to establish how much positional information is available from local olfactory sensation of remote odour sources.
This project was based around the technique of ultrasound elastography. In particular I was trying to develop a technique for estimating absolute stiffness at a small set of points in an ultrasound image plane by tracking the propagation of shear waves produced by a surface tap using standard ultrasound imaging hardware.
Most of my code can be found on my Github profile. Below are a selection of the possibly more generally useful repositories.
I am currently the teaching assistant for the coursework-based Machine Learning Practical. The Jupyter notebooks and Python framework we are developing for the course lab sessions are being distributed on the course Github repository. I am also a tutor for Machine Learning and Pattern Recognition. I have also previously tutored Information Theory and Probabilistic Modelling and Reasoning (PMR). There a couple of Jupyter notebooks with notes on PMR topics I made when tutoring on Github here.
I did a short tutorial on Hamiltonian Monte Carlo for the Institute for Adaptive and Neural Computation PIGlets discussion group. The slides are available here and an associated Jupyter notebook going through an example implementation is available on Github.
In my spare time I'm a keen hillwalker and mountaineer. I walked extensively with the Cambridge University Hillwalking Club while doing my undergraduate there. In the summer of 2012 I helped organise and took part in an expedition to the Tien Shan range in Kyrgyzstan with seven other CUHWC members. Since coming to Edinburgh I've joined the Edinburgh University Hillwalking Club and have started climbing regularly at the wall in the University sports centre.