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.