Lecturer in Data Science and Statistics, Department of Mathematical Sciences, University of Essex, United Kingdom.

Honorary Senior Research Associate in Medical Statistics, Bristol Medical School (PHS), University of Bristol, United Kingdom.

Director of the Data Science Courses at the Department of Mathematical Sciences, University of Essex.

Author of the Slope-Hunter: A robust method for collider bias correction in conditional genome-wide association studies. The method has been published in NATURE COMMUNICATIONS (2022).

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My expertise lies within Data Science, Medical Statistics and Bio-Statistics domains with extensive practice of:

  • Causal inference.
  • Machine learning.
  • Longitudinal data analyses.
  • Epidemiology.

My research projects have led to a track record of publications in prestigious world-leading academic journals.

I have a great passion for teaching Statistics and Data Science, and have taught them since 2004 in a variety of learning environments across three higher educational institutions in the UK and Egypt.

I am running a number of Statistical and Data Science courses at several locations across Europe. I do deliver courses on-sites as well. If you are representing an institution and interested in finding out more details, please feel free to contact me.

I have an extensive experience on writing software commands, mainly using R, for newly developed statistical techniques as well as for effectively applying existing methods. I am an author of a number of statistical and machine learning methods whose software packages are published on the CRAN, Github, and purpose websites such as the Slope-Hunter method. Moreover, I am the main developer, author/co-author for a number of web applications in bio-medical sciences.

Research Interests

My research interests cover a range of methodological and applied Statistical and Data Science areas.

A brief list of my research interests includes:

  • Predictive modelling.
  • Causal inferences and genetic epidemiology.
  • Machine learning.
  • Statistics for Public Health.
  • Classification and clustering.