Professor Wand is chiefly interested in the development of statistical methodology for finding useful structure in large multivariate data sets. Currently, his specific interests include expectation propagation, message passing algorithms, variational approximate methods, statistical methods for streaming data, generalised linear mixed models, and semiparametric regression.

He is also very interested in statistical computing and contributes to the field's main software repository, the Comprehensive R Archive Network.

• Computational statistics
• Expectation propagation
• Generalised linear mixed models
• Graphical models
• Message passing algorithms
• Monte Carlo Methods
• Nonparametric regression
• Real-time data analysis
• Semiparametric regression modelling
• Spatial statistics
• Variational approximations