I design machine learning algorithms that try to solve some of today's most challenging problems in computer science and statistics.

I adapt ideas from physics and the statistical sciences, and use them in algorithms that can be applied to areas such as: bioinformatics, artificial intelligence, pattern recognition, document information retrieval, and human-computer interaction.

Click on the following topics to see research descriptions and some papers:-

Nonparametric Bayes - powerful nonparametric text/document modelling
Variational Bayesian Methods - approximate Bayesian learning and inference
Bioinformatics - microarray analysis using variational Bayes
Embedded Hidden Markov Models - a novel tool for time series inference
Probabilistic Sensor Fusion - combining modalities using Bayesian graphical models
Collaborators - people I have worked with


With a wealth of data becoming available from gene microarray experiments, there is plenty of room for application of machine learning algorithms. My current work involves applying VB versions of feedback state-space models (see chapter 5 of my thesis) to microarray data to elucidate gene-gene interactions and new and exciting pathways for cell processes such as apoptosis and proliferation.