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

Quick links to collaborators

Hagai T. AttiasGolden Metallic
David M. BleiCS, Princeton
Zoubin GhahramaniGatsby Computational Neuroscience Unit, London UK
Nebojsa JojicMicrosoft Research, Seattle WA
Michael I. JordanComputer Science Division, UC Berkeley
Radford M. NealStatistics, Toronto ON
Carl E. RasmussenMax Planck Institute, Tübingen Germany
Sam T. RoweisComputer Science, Toronto ON
Yee Whye TehComputer Science Division, UC Berkeley
David L. WildKeck Graduate Institute, Claremont CA