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Dr Peter Giles

Data Scientist

School of Medicine

Overview

My research is focused on how the explosion of data that is collected and generated by researchers, clinical trials and the NHS can be combined and analysed to drive advances in treatment options for patients as well as advance our scientific understanding of disease.

To do this requires a number of underpinning technologies and techniques, so I have expertise in the design, deployment and management of high-performance computing infrastructure (including GPU analysis) and petabyte scale data storage as well as the bioinformatic and AI techniques needed to analyse and extract information from genomic, transcriptomic, radiomic and digital pathology datasets.

My current work is centred around exploiting the opportunities that Trusted Research Environments can unlock to bring together data collected for a single patient during their treatment journey and how we can then apply machine learning techniques to unlock information from these interlinked datasets.

Publication

2023

2021

2020

2019

2018

2016

2015

2014

2012

2010

2009

2008

2005

2002

2001

Articles

Thesis

Biography

Career overview

2022-present Professional Advisor for Data Science - Division of Cancer and Genetics, Cardiff University
2019-2022 Lead Bioinformatician and Bioinformatics Manager - Wales Gene Park, Cardiff University
2011-2019 Research Associate in NGS Bioinformatics - Wales Cancer Research Centre, Cardiff University
2005-2011 Research Fellow in Microarray Bioinformatics - Central Biotechnology Services, Cardiff University
2000-2001 Research Assistant in Bioinformatics and Computing - University of Wales College of Medicine

Education and qualifications

2005 - PhD Medicine, Cardiff University - Microarray-based expression profiling: improving data mining and the links to biological knowledge pools [supervisor Prof David Kipling]
2002 - Diploma in Biomedical Methods, University of Wales College of Medicine 
1999 - BSc (Hons) Pharmacology, Cardiff University

Specialisms

  • Data science
  • Bioinformatics and computational biology
  • High performance computing
  • Trusted research environments (TREs)
  • Machine learning