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School of Physical and Chemical Sciences

Dr Edward Hirst


Postdoctoral Research Assistant

Room Number: G. O. Jones Building, Room 604


Dr Edward Hirst is a postdoctoral researcher at Queen Mary, University of London, working with Prof David Berman at the Centre for Theoretical Physics, with affiliation to the Digital Environment Research Institute. He completed his PhD at City, University of London under the supervision of Prof Yang-Hui He, and prior to that a Masters degree at Imperial College London.


Research Interests:

Dr Hirst examines mathematical objects within theoretical physics, focusing on those related to string and gauge theories as well as the related algebraic geometry, in particular Calabi-Yau manifolds. His current work centres on applications of a variety of techniques from machine learning to databases of these objects, with the aim of uncovering interesting phenomena and interrelations. Furthermore, also examining how concepts from theoretical physics can be used to explain the successes of machine learning.


D. S. Berman, Y.-H. He, and E. Hirst, “Machine learning Calabi-Yau hypersurfaces,” Phys. Rev. D, vol. 105, no. 6, p. 066 002, 2022. doi: 10.1103/PhysRevD.105.066002. arXiv: 2112.06350 [hep-th]
J. Bao, S. Franco, Y.-H. He, E. Hirst, G. Musiker, and Y. Xiao, “Quiver Mutations, Seiberg Duality and Machine Learning,” Phys. Rev. D, vol. 102, no. 8, p. 086 013, 2020. doi: 10.1103/PhysRevD.102.086013. arXiv: 2006.10783 [hep-th].
J. Bao, Y.-H. He, E. Hirst, J. Hofscheier, A. Kasprzyk, and S. Majumder, “Hilbert series, machine learning, and applications to physics,” Phys. Lett. B, vol. 827, p. 136 966, 2022. doi: 10.1016/j.physletb.2022.136966. arXiv: 2103.13436 [hep-th].
J. Bao, Y.-H. He, and E. Hirst, “Neurons on Amoebae,” J. Symb. Comput., vol. 116, pp. 1–38, 2022. doi: 10.1016/j.jsc.2022.08.021. arXiv: 2106.03695 [math.AG]
P.-P. Dechant, Y.-H. He, E. Heyes, and E. Hirst, “Cluster Algebras: Network Science and Machine Learning,” Mar. 2022. arXiv: 2203.13847 [math.CO].

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