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. Attias | Golden Metallic | |
| David M. Blei | CS, Princeton | |
| Zoubin Ghahramani | Gatsby Computational Neuroscience Unit, London UK | |
| Nebojsa Jojic | Microsoft Research, Seattle WA | |
| Michael I. Jordan | Computer Science Division, UC Berkeley | |
| Radford M. Neal | Statistics, Toronto ON | |
| Carl E. Rasmussen | Max Planck Institute, Tübingen Germany | |
| Sam T. Roweis | Computer Science, Toronto ON | |
| Yee Whye Teh | Computer Science Division, UC Berkeley | |
| David L. Wild | Keck Graduate Institute, Claremont CA |