- Bao, J., He, Y.-.H. and Hirst, E. (2023). Neurons on amoebae. Journal of Symbolic Computation, 116, pp. 1–38. doi:10.1016/j.jsc.2022.08.021.
- Arias-Tamargo, G., He, Y.-.H., Heyes, E., Hirst, E. and Rodriguez-Gomez, D. (2022). Brain webs for brane webs. Physics Letters B, 833, pp. 137376–137376. doi:10.1016/j.physletb.2022.137376.
- Bao, J., He, Y.-.H., Hirst, E., Hofscheier, J., Kasprzyk, A. and Majumder, S. (2022). Hilbert series, machine learning, and applications to physics. Physics Letters B, 827, pp. 136966–136966. doi:10.1016/j.physletb.2022.136966.
- Bao, J., Hanany, A., He, Y.-.H. and Hirst, E. (2022). Some open questions in quiver gauge theory. Proyecciones (Antofagasta), 41(2), pp. 355–386. doi:10.22199/issn.0717-6279-5274.
- Berman, D.S., He, Y.-.H. and Hirst, E. (2022). Machine learning Calabi-Yau hypersurfaces. Physical Review D, 105(6). doi:10.1103/physrevd.105.066002.
- Bao, J., He, Y.-.H. and Hirst, E. (2021). Neurons on Amoebae. Journal of Symbolic Computation, 1, p. 6.
- Bao, J., Foda, O., He, Y.-.H., Hirst, E., Read, J., Xiao, Y. … Yagi, F. (2021). Dessins d’enfants, Seiberg-Witten curves and conformal blocks. Journal of High Energy Physics, 2021(5). doi:10.1007/jhep05(2021)065.
- He, Y.-.H., Hirst, E. and Peterken, T. (2021). Machine-learning dessins d’enfants: explorations via modular and Seiberg–Witten curves. Journal of Physics A: Mathematical and Theoretical, 54(7), pp. 75401–75401. doi:10.1088/1751-8121/abbc4f.
- Bao, J., Franco, S., He, Y.-.H., Hirst, E., Musiker, G. and Xiao, Y. (2020). Quiver mutations, Seiberg duality, and machine learning. Physical Review D, 102(8). doi:10.1103/physrevd.102.086013.
- Bao, J., He, Y.-.H., Hirst, E. and Pietromonaco, S. (2020). Lectures on the Calabi-Yau Landscape. Fields Institute Monographs.
Ed Hirst
Contact details
Address
Ed Hirst
City, University of London
Northampton Square
London EC1V 0HB
United Kingdom
Northampton Square
London EC1V 0HB
United Kingdom
About
Overview
PhD Student in the mathematical physics group of the mathematics department at City, University of London. Research work is with Prof Yang-Hui He into applications of machine-learning and data science techniques on problems arising in string and gauge theories. Primary interests within this are the relevant areas of algebraic geometry arising in string theory, particularly the Calabi-Yau landscape of manifolds.
Qualifications
- MSci Physics with Theoretical Physics, Imperial College London, United Kingdom, Oct 2015 – Jun 2019
Expertise
Geographic Areas
- Europe - Western
Research
Title of thesis: Machine-Learning and Data Science in String and Gauge Theories
Oct 2019 – Mar 2023
Summary of research
Application of advanced numerical and machine-learning data science techniques to problems arising within the algebraic geometry sector of string theories, most notably relating to Calabi-Yau manifolds and their classification.
External supervisor
- He, Y-H. City, University London.