Inter-disciplinary application areas have included research in signal processing, finance, climatology, systems biology, genomics and neurosciences, among others. Currently active areas include Bayesian methods and applications in financial econometrics (high-dimensional time series and large-scale portfolio studies). systems biology (single cell and single molecule dynamics, networks, imaging, basic pathway genomics), neuroscience (large-scale spatio-temporal modelling), and atmospheric chemistry (spatio-temporal systems and computer model emulation),

The BEST Award for Student Research

Stat@Duke group on Facebook

• Time Series: Modelling, Computation and Inference, by Raquel Prado & Mike, Chapman & Hall/CRC-Taylor Francis Group, 2010
• The Handbook of Applied Bayesian Analysis, edited by Tony O'Hagan & Mike, Oxford University Press, 2010
• Bayesian Forecasting and Dynamic Models (2nd edition), by Mike & Jeff Harrison, 1997
• Applied Bayesian Forecasting and Time Series Analysis, by Andy Pole, Mike & Jeff Harrison, 1994

• The Granger Centre for Time Series Econometrics, Nottingham University, UK
• The Institute for Integrating Statistics in Decision Sciences, George Washington University, DC
• The Institute of Statistical Mathematics, Tokyo, Japan-- the world's oldest statistical science research institute