A fan of mathematics, technology, music and the outdoors.

PhD candidate at Imperial College London researching machine learning and statistics applied to weather prediction.

Oxford graduate with professional experience as a data engineer in AI and fintech.

Background in statistics, machine learning, climate science and finance, with experience spanning both academic research and business applications.

I’m a PhD student at Imperial College London researching statistics and machine learning applied to weather prediction systems as part of the Mathematics for Future Climate CDT. My research theme covers generative modelling, extreme value theory and uncertainty quantification in the context of MLWP. My current focus is on harmonising generative AI modelling with extreme value theory to produce robust unsupervised learning models suitable for applications in climate and weather.

My professional experience includes data engineering and research roles in the tech and financial sectors, with particular expertise in unsupervised learning, forecasting, LLM implementation and unstructured data extraction. I’m available for technical consulting on data-driven projects.

Recent achievements include being awarded first place in the OpenWeather Challenge 2025 for my submission WIRE, as well as wins in two Society for Technological Advancement hackathons, where I developed an automated Bayesian prior elicitation system for non-technical experts and an AI geoengineering simulator based on machine learning weather models.

My previous studies include a BA in Economics and Management at the University of Oxford and an MSc in Statistics at Imperial College London.

Beyond work and academia, I’m a saxophonist who writes music and regularly performs across London. I also enjoy hiking, travelling and generally exploring nature!

Contact:

The best way to reach me is via email at ashley.turner24[at]imperial.ac.uk or send me a message on LinkedIn.