A person posing for a photo.

Joseph Hadley

Using machine learning to find better algorithms for getting to an equilibrium state in lattice simulations of gluon systems.

Joseph studied at the University of Nottingham, graduating in 2017 with a combined masters degree in Physics with Theoretical Physics. His final project was titled Scalar Fields in Cosmology, where a number of scalar fields were introduced to a toy model of cosmology as a form of quintessence. Simulations were performed to show that scalar fields added in this way can mimic the effect of dark energy, leading to accelerated expansion of the universe at late times.

Within LIV.INNO Joseph will continue to work on simulations. The strong nuclear force is difficult to tackle analytically, and lattice simulations are often needed. There is a need for algorithms which can get a gluon lattice system to an equilibrium state, “warmed through”, in an efficient way. That might mean more quickly than current algorithms, less computationally intensive, or able to be used on larger systems. Joseph will make use of machine learning in finding these algorithms.

Since graduating, Joseph has worked in commercial data applications at Experian, and targeted fundraising at Nottinghamshire Hospice. He is excited to be back in physics.