I am a research fellow in the Department of Statistics and Applied Probability at the National University of Singapore, where I am part of Alex Thiery's research group. My research is focused on developing methods for performing efficient approximate inference in complex probabilistic models.
Research interests: Markov chain Monte Carlo methods, Hamiltonian Monte Carlo, approximate Bayesian computation, data assimilation, inverse problems.
Matthew M. Graham and Alexandre H. Thiery
Matthew M. Graham and Amos J. Storkey
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
Matthew M. Graham
PhD thesis, University of Edinburgh
Matthew M. Graham
MSc by Research dissertation, University of Edinburgh
Seminar at Department of Statistics and Applied Probability, National University of Singapore.
Seminar at Department of Statistics and Applied Probability, National University of Singapore.
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
My PhD project was focussed on developments to Markov Chain Monte Carlo (MCMC) methods. I specifically considered 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.
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 was the teaching assistant for the coursework-based Machine Learning Practical in the 2016-2017 academic year. The Jupyter notebooks and Python framework I helped co-develop for the course lab sessions are available at the course Github repository. I have also previously tutored for Machine Learning and Pattern Recognition, 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 during my undergraduate studies. 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. During my PhD in Edinburgh I was a member of Edinburgh University Hillwalking Club and also began climbing in and outdoors more regularly (though still sadly not all that often!).