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Department of Applied Mathematics and Theoretical Physics

I am a PhD student under supervision of Professor Carola Schönlieb at the Cambridge Image Analysis Group within DAMTP. I am also a member of the Cantab Capital Institute for the Mathematics of Information and the Maths4DL programme.

I am generally interested in applied mathematics, specifically problems arising at the intersection of optimization, deep learning, geometry, and inverse problems.

Publications

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
Symplectic Neural Flows for Modeling and Discovery
P Canizares, D Murari, C-B Schönlieb, F Sherry, Z Shumaylov
(2024)
Hamiltonian Matching for Symplectic Neural Integrators
P Canizares, D Murari, C-B Schönlieb, F Sherry, Z Shumaylov
(2024)
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
Z Shumaylov, P Zaika, J Rowbottom, F Sherry, M Weber, C-B Schönlieb
(2024)
AI models collapse when trained on recursively generated data.
I Shumailov, Z Shumaylov, Y Zhao, N Papernot, R Anderson, Y Gal
– Nature
(2024)
631,
755
Quantum initial conditions for curved inflating universes
MI Letey, Z Shumaylov, FJ Agocs, WJ Handley, MP Hobson, AN Lasenby
– Physical Review D
(2024)
109,
123502
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Z Shumaylov, J Budd, S Mukherjee, C-B Schönlieb
(2024)
Data-Driven Convex Regularizers for Inverse Problems
S Mukherjee, S Dittmer, Z Shumaylov, S Lunz, O Öktem, CB Schönlieb
– 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024)
(2024)
00,
13386
Provably Convergent Data-Driven Convex-Nonconvex Regularization
Z Shumaylov, J Budd, S Mukherjee, C-B Schönlieb
(2023)
The Curse of Recursion: Training on Generated Data Makes Models Forget
I Shumailov, Z Shumaylov, Y Zhao, Y Gal, N Papernot, R Anderson
(2023)
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Research Group

Mathematics of Information (Applied)

Room

F0.01