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|