Daniel Murnane

Daniel Murnane is a postdoctoral researcher, working with the ExaTrkX group on exascale machine learning for particle physics tracking. He is currently researching the use of graph neural networks, metric learning, and GPU acceleration techniques to prepare for next-generation tracking problems, such as those encountered at the High-Luminosity Large Hadron Collider. He received his PhD at Adelaide University in 2019, studying the viability of composite extensions to the Higgs model as Beyond Standard Model physics.

In his spare time, he writes and performs music, and develops ML solutions for projects including bias detection in journalism and behaviour sentiment detection for online learning.