Dr Simon Smart MA (Cantab) PhD

Dr Simon Smart

Department of Applied Health Sciences
Research Software Engineer

Contact details

Address
Department of Applied Health Sciences
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Simon is a Research Software Engineer specialised in the R programming language. His work supports making advanced statistical methods more accessible to users and ensures that statistical software is robust, reliable and reproducible. His work is all open source and can be found on GitHub.

Qualifications

  • PhD in Plant Sciences, University of Cambridge, 2016
  • MA (Cantab) in Natural Sciences, University of Cambridge, 2014

Biography

Simon began his career as an agricultural scientist specialising in the physiology and agronomy of potato crops. After gaining his PhD from the University of Cambridge in 2016, he worked at NIAB (formerly the National Institute of Agricultural Botany) rising to the position of Senior Research Associate, until departing in 2022. His interest in statistical programming and data science grew over the years and led to the development of dashboards for forecasting the yield of potato and tomato crops and software to automate the collection and processing of data.

Prior to his current position, he was a Software Developer at the University of Leicester where he developed web-based applications for conducting complex analyses. He developed the shinyscholar R package for developing applications which can be reproduced independently of the application. The package also makes is easier for developers to follow best practices in software development such as using a modular architecture and automated testing. It was applied to develop Disagapp, an application for disease mapping and to refactor MetaInsight, an application for conducting network meta-analysis.

In his current role he is part of the Geostatistics for Public Health group where he supports Professor Emanuele Giorgi and Dr Claudio Fronterre. Together they are developing RiskMap, an R package for model-based geostatistics and applying this to modelling neglected tropical diseases.