Dr Edward Hirst

Edward Hirst

Postdoctoral Research Assistant

School of Physical and Chemical Sciences
Queen Mary University of London
inspirehep.net/authors/1791403
Centre for Fundamental Physics
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Research

String Theory, Differential Geometry, Algebraic Geometry, Machine Learning

Interests

Generation and analysis of data relevant to problems in string theory and more general high-energy theoretical physics; especially for the related geometries used in compactification: Calabi-Yau and G2 manifolds. In performing the analysis a wide range of machine learning techniques are implemented, notably highlighting recent research directions using neural networks to find and approximate metric solutions on various geometries.