A PhD student within the UCL Energy & AI research lab, Ayrton’s work focuses on applying deep learning to UK electrical imbalance price/volume forecasting. His commercial experience includes working on analysis for London Array and Aurora Energy Research, as well as a large number of freelance projects within the wind sector specialising in electricity markets. Prior to working as an energy focussed data scientist he achieved a first class degree in chemical engineering.
Clement Attwood is a research fellow at the UCL Energy Institute. His research is focused on the application of Artificial Intelligence for monitoring sustainable development and decarbonisation of the energy system. With over a decade of applied research and analytics experience, his work is now focussed on applying machine learning to increase grid resilience through grid frequency and providing online coaching to home energy users to help them save energy. Before his time at UCL, Clement was a research consultant with UNDP and the International Institute for Sustainable Development.
Laurence Watson is a data scientist and analyst with broad experience in energy and climate change. He has previously modelled coal power asset stranding at Carbon Tracker and created methodologies to use satellite data to track coal emissions. He was head of technology at Ember, an NGO focused on reforming carbon markets. Laurence also has a breadth of experience within energy policy, working in Westminster for several MPs as a researcher across energy and industrial strategy briefs. He has a degree in physics from the University of Cambridge and an MSc in Energy Systems from UCL.
Connor Galbraith is a consultant and data scientist with wide-ranging experience in full-stack development, data engineering, and machine learning, and has delivered technical projects to clients including BAE Systems, the World Bank, and the UK Government. Outside of FEA, Connor is also a consultant at UMAS International, leading the development of cloud-based technical capabilities to drive decarbonisation in the maritime sector. Previously, he has held research and teaching roles at the UCL AI and Energy Systems group, and specialised in energy, aerospace, and electrical power systems whilst reading engineering at the University of Cambridge.
Dylan Johnson is a researcher at the UCL MaaSLab. His research focuses on the modelling and simulation of innovative transportation systems. His work includes MaaS product design, demand and supply modelling, GPS trace analysis, spatial analysis, and machine learning in transportation. Additionally, Dylan works as a teaching assistant at the UCL AI and Energy Systems group. Dylan has experience working with the UK Green Party, providing energy expertise and policy advice. Dylan holds a distinction in the MSc Energy Systems from UCL and an economics degree from Durham University.