Advanced Research Computing

Prof James Hetherington

Prof James Hetherington

Director of the UCL Advanced Research Computing Centre

UCL Centre for Advanced Research Computing

Vice-President (Operations)

Joined UCL
3rd Oct 2011

Research summary

I lead a variety of research projects in e-Science: the study of how computers, data and software are transforming science and scholarship. In particular, I focus on reliability questions in computationally intensive research, such as trustworthiness, auditability, reliability, security, verifiability and fault tolerance.

Teaching summary

My department provides teaching and training on software development, data stewardship, data science and infrastructure development for research at a variety of levels to students and researchers across college, from the basics of version control, through software testing methodologies, the use of a variety of languages and libraries for scientific computing, dev-ops, software defined infrastructure and cloud, to advanced high-performance computing and the use of modern tools for managing, analysing and organising large and complex data.


University of Cambridge
Doctorate, Doctor of Philosophy | 2002
University of Cambridge
Other higher degree, Master of Natural Science | 2000
University of Cambridge
First Degree, Bachelor of Arts | 1998


I am Director of the Advanced Research Computing Centre at University College London. UCL ARC has a hybrid mission: to provide the computing, data, software and networking infrastructure and skills that empower computational science and digital scholarship across the university and its partners, and to deliver world-leading research in digital research infrastructures and their application. Prior to rejoining UCL to lead ARC in 2021 I have held a variety of senior leadership roles in computational science, digital research, and data science.

I was Director of Research Engineering at the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence. I founded, grew and led a team of thirty research software engineers and data scientists contributing to a huge range of data- and compute-intensive research. The team continue to build and use tools to analyse and present large datasets, and create complex models running on state of the art supercomputers. In particular, I directed the "Tools, Practices and Systems" research programme within the UK's strategic priority research programme "AI for Science, Engineering, Health and Government".

During the acute phase of the Coronavirus pandemic I was seconded from the Alan Turing Institute as Chief Data Science Advisor to the Joint Biosecruity Centre, the quantitative analysis and assessment hub of the UK Coronavirus Response. I was lead advisor on digital twins, mathematical, statistical and computational modelling, machine learning, research software engineering and trusted research environments, and worked very closely with the most senior officials.

I was the Director of Digital Research Infrastructure at UK Research and Innovation, the UK's national research and innovation agency. I led on strategy for the software, supercomputers, skills, data services and clouds that underpin computational science and digital scholarship in the UK.

I was founding head of UCL’s Research Software Engineering Group, now part of the new ARC department. It was the first such group in a UK university. Fields addressed included machine learning for intensive care, ancient Mesopotamian history, graph theoretical approaches to modeling chemical catalysis, computer vision for astronomy, trans-oceanic journalistic exchanges, data centric engineering, brain blood flow simulations and DNA crime scene analysis. This new model for applied computational research groups in universities, pioneered under my leadership, has now been adopted by research intensive universities across the globe.

Prior to these roles, I held a number of individual contributor roles in digital research in industry and academia, including with Mathworks, the makers of Matlab, at AMEE, a climate change data science startup, and in postdoctoral roles in UCL in computational physiology. My PhD in theoretical and computational physics from Cambridge University focused on computational tools for predicting experimental signatures of supersymmetric theories.