Yacoub Hendi
Ph.D. Student in Mathematics
Uppsala University, Sweden
📧 Email: yacoub.hendi@math.uu.se
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Research Interests
My research explores the intersection of machine learning and geometry. On one hand, I apply machine learning techniques to compute numerical approximations on manifolds such as Ricci-flat metrics on Calabi–Yau manifolds. On the other hand, I study the geometric properties of neural networks to better understand their efficiency.
Education
Ph.D. in Mathematics (2023– )
Uppsala University
Advisor: Prof. Magdalena Larfors
Master of Mathematics (2021–2023)
Thesis: Computation of the superpotential for monotone Lagrangian submanifolds in products of complex projective lines
Advisor: Assoc. Prof. Luis Diogo
Bachelor of Mathematics (2018–2021)
Thesis: On The Prime Number Theorem
Advisor: Prof. Andreas Strömbergsson
Bachelor of Computer Science (2018–2021)
Thesis: Parameterized Verification under The Total Store Order Memory Model is EXPTIME-Complete
Advisor: Prof. Parosh Abdulla
Activities
Imperial College London, London
Umeå University, Umeå
Rényi Institute, Budapest
Rényi Institute, Budapest
Umeå University, Umeå
Talks
PhD seminar series, Uppsala University, Uppsala
Summer School: Invitation to Complex Geometry, Rényi Institute, Budapest
GeUmetric Deep Learning Workshop, Umeå
Publications & Papers
Y. Hendi, M. Larfors, M. Walden
Machine Learning: Science and Technology, 6(1), 015050 (2025) Link
P.A. Abdulla et al.
TACAS 2023, LNCS 13993, Springer (2023) Link