معرفی
Yang-Hui He is a Professor at the School of Mathematics, Statistics and Actuarial Science, City, University of London. His research spans Machine Learning, String Theory, and Algebraic Geometry, with a focus on applying data science to mathematical and physical problems.
His research interests include the intersection of Calabi-Yau manifolds, quiver gauge theories, and number theory. He has pioneered the use of neural networks to classify spacetime geometries, predict curve invariants, and analyze vacuum configurations in string phenomenology.
Recent publications explore machine learning applications in classifying elliptic curves, modeling brane webs, and understanding quiver dynamics. His work often bridges abstract mathematics with computational methods, leveraging AI to uncover patterns in high-dimensional data from theoretical physics.
Collaborations include co-authors such as K-H. Lee, J. Bao, E. Hirst, and V. Jejjala, reflecting a multidisciplinary approach to unsolved problems in supersymmetric theories and cosmology.
