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Personal profile

Research interests

  • Parameter estimation for subsurface flow problems
  • Data Assimilation and nonlinear filtering
  • Bayesian uncertainty quantification and model comparison
  • A posteriori error estimation
  • Mesh generation and mesh adaptivity
  • Carbon Capture and Sequestration


  • August 2017, Associate Professor, Institute of Petroleum Engineering, Heriot-Watt University, Scotland
  • August 2013 - July 2017, Assistant Professor, Institute of Petroleum Engineering, Heriot-Watt University, Scotland
  • June 2012 - July 2013, Research Fellow, ICES, The University of Texas at Austin, USA
  • 2010-2012, Research Associate, ESE, Imperial College London, UK
  • 2007-2010, Assistant Professor, Al-Azhar Univeristy, Egypt
  • 2007 Ph.D., McMaster University, Ontario, Canada
  • 2002 MASc., McMaster University, Ontario, Canada
  • 1999 BASc., Al-Azhar Univeristy, Cairo, Egypt

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

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Research Output 2004 2019

Hydrogeophysical parameter estimation using iterative ensemble smoothing and approximate forward solvers

Köpke, C., Elsheikh, A. H. & Irving, J., 20 Mar 2019, In : Frontiers in Environmental Science. 7, 34.

Research output: Contribution to journalArticle

Open Access
ground penetrating radar
data assimilation
travel time

Quantification of prediction uncertainty using imperfect subsurface models with model error estimation

Rammay, M. H., Elsheikh, A. H. & Chen, Y., 6 Mar 2019, In : Journal of Hydrology.

Research output: Contribution to journalArticle

Accounting for model error in Bayesian solutions to hydrogeophysical inverse problems using a local basis approach

Köpke, C., Irving, J. & Elsheikh, A. H., Jun 2018, In : Advances in Water Resources. 116, p. 195-207 13 p.

Research output: Contribution to journalArticle

Open Access
inverse problem
Markov chain
statistical distribution
ground penetrating radar
travel time

A machine learning approach for efficient uncertainty quantification using multiscale methods

Chan, S. & Elsheikh, A. H., 1 Feb 2018, In : Journal of Computational Physics. 354, p. 493-511

Research output: Contribution to journalArticle

Open Access
chaotic dynamics