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Writing & Teaching
I translate quantitative energy research and applied modelling into clear public analysis and structured learning resources.
Writing
Through The Energy Data Scientist on Substack, I publish independent analysis of energy markets, energy systems and quantitative modelling.
I examine current developments and explain the economics, data, assumptions and modelling choices behind the conclusions.
Teaching
I created The Energy Data Scientist, a self-paced professional education programme for students, researchers and energy professionals seeking to develop quantitative skills for the energy sector.
The programme focuses on energy economics and energy finance, using data science, machine learning and optimization. It includes online courses and downloadable resources including code, models and datasets, with individual guidance on learning, code, projects, research and career development.
I was awarded Associate Fellowship of the Higher Education Academy (AFHEA) in 2026.
At Imperial College London, I supervised and mentored PhD and MSc students in areas including power systems, smart-grid technologies, energy storage, optimisation, machine learning and network planning under uncertainty.
My educational resources draw on methods used in peer-reviewed research, energy consultancy and applied modelling projects.
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