Hello There!
I am a PhD student in theoretical machine learning at Harvard SEAS supervised by Professor Cengiz Pehlevan, and a recent graduate from the MSc Theoretical Physics program at Perimeter Institute.
My background and research interests span a wide range of exciting problems in applied mathematics, from early-universe cosmology to machine learning and machine learning for physics. My current projects focus on discretisations and discrete symmetry in relativistic geometries with Professors Latham Boyle and Jasper van Wezel, and group-equivariant neural network architectures with Professor Siamak Ravanbakhsh at Mila.
PhD in Applied Mathematics, Beginning Fall 2023
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
Publications
Projects
Towards the goal of constructing discretisations of spacetime models that preserve as large of a discrete subgroup of Poincare symmetry as possible, we investigate lattices in maximally-symmetric relativistic geometries (i.e. Minkowski, de Sitter, and Anti-de Sitter spaces), and explore their properties and symmetry groups.
Supervisors: Professor Latham Boyle, Professor Jasper van Wezel.
Colleagues: Lizzy Rieth, Dr Felix Flicker.
Master’s Thesis. 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 Latham Boyle.
Investigating tree-level scatting 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.
Supervisor: Professor Freddy Cachazo.
Colleagues: Dawit Belayneh, Raquel Izquierdo Garcia, James Munday.
Extending recent work pioneered at PiQuIL in approximating the groundstate wavefunction of a quantum lattice system using Recurrent Neural Networks: Investigated the affect of error and noisiness of the quantum data on the accuracy of the wavefunction and other physical quantities.
Supervisors: Professor Roger Melko, Schuyler Moss.
To generalise results in cosmological inflation to include non-flat universes and non-eternal inflation, a novel comoving curvature perturbation variable is proposed and analysed. Novel initial conditions are proposed by setting the vacuum using the renormalised stress energy tensor.
Supervisor: Dr Will Handley.
Colleagues: Zakhar Shumaylov.
Examining the extent to which text data (such as financial reports, news articles, and search mentions) can predict the closing stock price of given companies. Text data was analysed using topic modeling to extract relevant features and recurrent neural networks to model time-dependence in the data sets.
Supervisor: Dr Chris Ketelsen.
Colleagues: Morgan Allen, Colton Williams, Aniq Shahid.
