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I am a Research Fellow in Numerical Analysis.

My research interests lie at the intersection between numerical analysis and deep learning. I primarily focus on the mathematical foundations of deep learning to discover mathematical models (partial differential equations) from data, and the development of novel and theoretically justified numerical techniques.

I am a member of the Scientific Artificial Intelligence (SciAI) Center supported by the Office of Naval Research (ONR).

Publications

Multiplicative Dynamic Mode Decomposition
N Boullé, MJ Colbrook
(2024)
Randomized Nyström approximation of non-negative self-adjoint operators
D Persson, N Boullé, D Kressner
(2024)
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
TJB Liu, N Boullé, R Sarfati, CJ Earls
(2024)
Operator learning without the adjoint
N Boullé, D Halikias, SE Otto, A Townsend
(2024)
On the Convergence of Hermitian Dynamic Mode Decomposition
N Boullé, MJ Colbrook
(2024)
A Mathematical Guide to Operator Learning
N Boullé, A Townsend
(2023)
Multivariate rational approximation of functions with curves of singularities
N Boullé, A Herremans, D Huybrechs
(2023)
Elliptic PDE learning is provably data-efficient.
N Boullé, D Halikias, A Townsend
– Proceedings of the National Academy of Sciences
(2023)
120,
e2303904120
Principled interpolation of Green's functions learned from data
H Praveen, N Boullé, C Earls
– Computer Methods in Applied Mechanics and Engineering
(2023)
409,
115971
Elliptic PDE learning is provably data-efficient
N Boullé, D Halikias, A Townsend
(2023)
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Research Group

Cambridge Image Analysis

Room

F2.05

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