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

Inference using Embedded Hidden Markov Models

Embedded HMMs, invented by Radford Neal, constitute a new type of inference tool for sequential data that will allow many filtering, prediction, and control problems to be tackled and solved in a very novel way. They are an elegant generalisation of the particle filtering and smoothing procedures that are currently used in non-linear systems. Embedded HMMs efficiently perform inference in non-linear systems by temporarily embedding a tractable (finite-state) HMM in the non-linear hidden state-space of the model.

So far the embedded HMM has been applied to the tasks of robot localisation, speech analysis, and recovering 3-dimensional structure from human motion sequences (ongoing work). In the future we hope to apply it to more general inference tasks in the graphical models framework.