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Faculty of Mathematics

 

I am a Research Fellow at Jesus College, Cambridge, and an EPSRC ProbAI Doctoral Prize Fellow at DAMTP.

I did my PhD in Mathematics of Information at the University of Cambridge as a Trinity Henry Barlow Scholar, supervised by Professor Carola Schönlieb in the Cambridge Image Analysis Group within DAMTP, as a member of the CCIMI and the Maths4DL programme. Before that, I completed an MMath at Cambridge. During my PhD I was a Student Researcher at Google DeepMind in Mountain View, working on inverse lithography, and an intern at Apple in Cambridge, working on model compression.

I am generally interested in applied mathematics, particularly problems arising at the intersection of optimization, deep learning, geometry, and inverse problems. During my PhD I focused on geometric deep learning and physics-informed machine learning.

Publications

Symplectic Neural Flows for Modeling and Discovery
P Canizares, D Murari, C-B Schönlieb, F Sherry, Z Shumaylov
– SIAM Journal on Scientific Computing
(2026)
48,
C1220
Non-Euclidean Gradient Descent Has Many Edges of Stability
P Zaika, Z Shumaylov
(2026)
Mathematics in the age of artificial intelligence: A primer on key topics in the mathematical sciences that underpin AI
D Leslie, M Anjos, O Anosova, R Bearon, M Calder, J Carrillo, A Champneys, T Coates, S Cohen, J Enright, A Etheridge, D Higham, S Jaggi, V Kurlin, R Leese, V Maguire Rajpaul, C Marr, C Nemeth, D Nicolau, C Offen, L Pareschi, M Pereyra, Y Roudi, C-B Schönlieb, Z Shumaylov et al.
(2026)
Muon is Not That Special: Random or Inverted Spectra Work Just as Well
Z Shumaylov, ND Costa, P Zaika, B Mucsányi, A Massucco, Y Gelberg, C-B Schönlieb, Y Gal, P Hennig
(2026)
Learning regularization functionals for inverse problems: a comparative study
J Hertrich, HS Wong, A Denker, S Ducotterd, Z Fang, M Haltmeier, Ž Kereta, E Kobler, O Leong, MS Salehi, C-B Schönlieb, J Schwab, Z Shumaylov, J Sulam, GS Wache, M Zach, Y Zhang, MJ Ehrhardt, S Neumayer
– Handbook of Numerical Analysis
(2026)
27,
85
When is a System Discoverable from Data? Discovery Requires Chaos
Z Shumaylov, P Zaika, P Scholl, G Kutyniok, L Horesh, C-B Schönlieb
(2025)
On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization
Z Shumaylov, V Tsiaras, Y Stylianou
(2025)
Data-driven approaches to inverse problems
C-B Schönlieb, Z Shumaylov
(2025)
Author Correction: AI models collapse when trained on recursively generated data
I Shumailov, Z Shumaylov, Y Zhao, N Papernot, R Anderson, Y Gal
– Nature
(2025)
640,
e6
LIE ALGEBRA CANONICALIZATION: EQUIVARIANT NEURAL OPERATORS UNDER ARBITRARY LIE GROUPS
Z Shumaylov, P Zaika, J Rowbottom, F Sherry, M Weber, CB Schönlieb
– 13th International Conference on Learning Representations Iclr 2025
(2025)
13401
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Room

F1.14

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