Dr. John Raine

Performing statistical analyses and developing machine learning models for use in particle physics as a member of the ATLAS Collaboration. Leading a team of junior researchers in an interdisciplinary group focussing on the development of new machine learning and AI techniques for particle physics, resulting in over 20 peer reviewd publications. Key focus on data driven and deep generative models for use as fast surrogate simulators and probabilistic modelling for regression and process modelling

Appointed coordinator of the Machine Learning forum at the ATLAS Collaboration for a two year term. Providing key expertise and insight on Machine Learning to members of the collaboration and the management structure. Organising seminars, workshops and tutorials on the topic of Machine Learning and AI.

Performing statistical analysis, machine learning research and software development as a member of the ATLAS Collaboration at CERN. Supervision of multiple PhD and Masters students, including project management in an international setting. Working with deep learning models applied to big data and deployment into a production framework in C++. Focus on generative models for surrogate simulation and enhancing sensitivity in data analyses whilst maintaining robustness to systematic uncertainties.

Search for the Higgs boson in association with top quarks with the ATLAS Experiment. Software development for data processing pipelines and integration of machine learning techniques. Devlepoment of machine learning techniques on tabular data, including feature engineering. Performing statistical analysis on Monte Carlo Simulation and real data. Two years spent on a long term attatchment at CERN, near Geneva, Switzerland.

Integrated Masters inclusive of Bachelor studies with a four year duration. One year spent abroad at the Technical University of Munich. Masters project on the development of software in C# for interpreting data collected from Neutron Refrectometry experiments.