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Georgios Karagiannis picture   Georgios P. Karagiannis   
  (  Giórgos /ˈʝoɾ.ɣos/  )
 
  Associate Professor in Statistics

  Department of Mathematical Sciences
  Office MCS3088
  Durham University
  Stockton Road
  Durham DH1 3LE
  UK
  georgios.karagiannis@durham.ac.uk,
  georgios.stats@gmail.com

I am Associate Professor in Statistics at the Department of Mathematical Sciences at Durham University in UK.

I have worked as a postdoctoral researcher in the Department of Mathematics of the Purdue University, and as a postdoctoral researcher in the Uncertainty Quantification group in the Pacific Northwest National Laboratory in USA.

I hold a PhD degree in Mathematics (Statistics) from the School of Mathematics at the University of Bristol, and a BSc degree in Statistics from the Department of Statistics at the Athens University of Economical and Business studies.

I am a Bayesian statistician with particular research interests in the development of methods for statistical modelling to address problems in uncertainty quantification and spatial statistics,  statistical computing to facilitate inference in complex statistical models, and statistical machine learning.

A number of my recent research projects/developments aim to address modern statistical challenges such as `Big Data' and High-Dimensional problems one can meet in real applications, while they can be implemented in parallel computing environments.

What's new this term:
  • A new paper in statistics has been accepted in Journal of the Royal Statistical Society: Series C with title "Multifidelity computer model emulation with high-dimensional output: An application to storm surge" in collaboration with researchers from University of Cincinnati, and Duke University
  • A new paper in statistics has been accepted in  Annals of Applied Statistics with title "Ice Model Calibration Using Semi-continuous Spatial Data" in collaboration with researchers from University of Cincinnati, University of Nebraska-Lincoln, and Penn State University
  • A new interdisciplinary paper has been published in Computation with title "Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework" in collaboration with researchers from Purdue University, and the Pacific Northwest National Laboratory.