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