I’m Mary, currently an applied math PhD student working on theoretical machine learning with Professor Cengiz Pehlevan.

I’ve enjoyed working in a wide range of mathematics research areas, from early-universe cosmology to group theory to ML4Physics. During my PhD, I’m interested in describing the emergence of neural network properties from complex interactions between operations, particularly in the context of structured data.

I’m always happy to discuss my work and related topics, please reach out! I’m also on linkedin, github, and X.

Education

PhD in Applied Mathematics, 2023 - Present

Harvard University, School of Engineering and Applied Sciences

MSc in Theoretical Physics, 2022 - 2023

Perimeter Institute for Theoretical Physics

BA in Mathematics, 2018 - 2022

University of Cambridge, St Johns College

**Supervisor:** Professor Latham Boyle. **Master’s Essay.** Towards constructing a mathematical framework to generalise the use of reflection groups in classifying discrete symmetries of Lorentzian spaces. We present a generalisation of the notion of crystallographic symmetry, an important property in the classical study of lattices and reflection groups, and then demonstrate substantial differences between reflection groups in Euclidean spaces vs Lorentzian spaces.

**Supervisor:** Professor Freddy Cachazo. Investigating tree-level scattering amplitudes for gluons in Yang-Mills. By utilising colour decomposition, we consider partial amplitude formulas in the case of 3 negative-helicity gluons; in particular, we study their singularity structure using projective geometry.

**Supervisors:** Professor Roger Melko, Schuyler Moss. Extending recent work pioneered at PiQuIL in approximating the groundstate wavefunction of a quantum lattice system using Recurrent Neural Networks: Investigated the effect of error and noisiness of the quantum data on the accuracy of the wavefunction and other physical quantities.