Adam began his undergraduate MPhys in 2013 at the University of Liverpool. His Masters project was aimed at improving the analysis of the rare Higgs to dimuon decay by exploring Boosted Decision Tree techniques. The idea was to explore these techniques to separate background and signal more effectively, ready for the LHC upgrade.
After a year in industry, applying the same data science concepts to healthcare, he is now undergoing a PhD to explore deeper into how machine learning and big data methods can improve the analysis of discovering new particles at the ATLAS detector. The project especially looks at searches for rare Higgs decays involving a Dark Matter candidate known as the Axion.
Adam is aware of how much impact machine learning and big data have both in science and industry and is keen to explore just how much can be achieved using the vast amount of data available at the LHC.