Christopher Yeates

Christopher Yeates has had a varied career in research and data science. In 2022, they began working as a Data Scientist at Stenon. From 2019-2022, they worked as a Research Scientist at GFZ-Potsdam, where they were part of the Helmholtz-Initiative Climate Adaptation and Mitigation project, optimizing transportation networks and storage solutions for national CO2 net emission reduction. Christopher also compared current heuristic graph methods for achieving minimum-cost pipeline networks. From 2015-2019, they worked on their PhD Thesis at IFP Energies Nouvelles, researching multi-scale flow dynamics in porous media in the context of Enhanced Oil Recovery. Christopher used high-frequency high-resolution acquisition, dynamic bubble tracking, and a machine learning approach to link pore-scale structural parameters to flow behaviour. In 2015, they also completed an internship at IFP Energies Nouvelles. In 2014, they completed an internship at CEA.

Christopher Yeates has a comprehensive educational background. Christopher obtained a Bachelor of Science in Physics from the University of Birmingham between 2009 and 2012. Christopher then went on to earn a Master's degree in Physics from Sorbonne Universit\u00e9 between 2013 and 2015. Finally, they completed their Doctor of Philosophy in Geoscience from Sorbonne Universit\u00e9 in 2019. In addition to their formal education, Yeates has obtained several certifications, including Applied Machine Learning in Python from Coursera in October 2018, Introduction to Data Science in Python from Coursera in August 2018, Energy Economy from IFP Training in April 2017, and Brut - Raffinage - Produits - Sch\u00e9mas de Fabrication from IFP Training in February 2016.