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Multivariate and High Dimensional Data


This theme has a research programme that encompasses both the theoretical and methodological problems encountered when analysing multivariate and high dimensional data. Much of the work in the area is driven by advances in technology in various application fields, where new forms of data with unprecedented levels of heterogeneity and complexity are in a modern setting collected routinely.

Current application problems that the group works on include medical imaging and near-infrared spectroscopy.

Theme members

Name Position  Selected Applications 
Tom Fearn Professor (Theme Lead) Near infrared spectroscopy
Mark Girolami Professor  
Serge Guillas Reader  
Christian Hennig Senior Lecturer  
Sofia Olhede Professor  Medical imaging, Oceanography, Ecology 
Ricardo Silva Lecturer   
Jinghao Xue  Lecturer  
Simon Harden PhD Student   
Ying Zhang PhD Student  

Recent Grants

  • Computational Statistics and Cognitive Neuroscience - EPSRC EP/H024875/1 - 2009 - 2011
  • Advancing Machine Learning Methodology for New Classes of Prediction Problems - EPSRC EP/F009429/1 - 2007 - 2011
  • The Molecular Nose - EPSRC EP/E032745/1 2007 to 2011
  • Developments of Multiple Kernel Methods for Produce Recognition - NCR Laboratories - 2011 to 2012
  • EPSRC Leadership Fellowship High Dimensional Models for Multivariate Time Series Analysis (EP/I005250/1) Oct. 2010–Sept. 2015 £1,220,055.
  • NERC Studentship (NE/F013051/1) Oct. 2008– Sept. 2012 £71,343. Multiscale Analysis of Integrated and Species-Specific Response to Climate Forcing and Eutrophication in Northern European Seas
  • EPRSC Network Grant (EP/F031157/1) Jan. 2008–Dec. 2010 £ 102,403. Network on multiScale Information, RePresentatIon and Estimation -- (INSPIRE)

Page last modified on 09 nov 11 10:51