Quantum computing PhD with experience in numerical methods and applied machine learning. I specialise in differentiable programming and tensor networks, with keen interest in applying these techniques in frontier AI models.

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Experience

Silicon Quantum Computing — PhD student (2021–2026)

University of New South Wales, Sydney

  • Designed, built, and tested a novel reservoir computing device, “Watermelon”, based on an array of phosphorus-doped silicon quantum dots, which became a primary revenue stream for the company
  • Created a differentiable simulator using PyTorch for tuning and designing quantum devices
  • Derived and implemented an end-to-end differentiable pipeline for eigensolving in tensor network algorithms

Optiver — Quantitative researcher intern (November 2023 – February 2024)

Sydney

  • Completed accelerated training in market making and quantitative trading
  • Evaluated the viability of a news-based trading signal using Bloomberg headline data
  • Developed improved estimators of intermediate-scale volatility in global equity indices

Ocado Technology — Innovation engineer (April – December 2020)

Hatfield

  • Applied representation learning techniques to Ocado product image data
  • Evaluated multiphysics simulation packages and advised on their usage

Ocado Technology — Machine learning internship (July – December 2019)

Hatfield

  • Built, tested and integrated an image segmentation model on a robot platform for autonomous navigation in complex environments

University of Oxford — MPhys research project (2018–2019)

Oxford

  • Trained a model on satellite data to detect Pockets of Open Cells in clouds on an unprecedented scale
  • Paper resulting from subsequent analysis won the climate change AI workshop best paper award at ICML 2019

Centre for Applied Superconductivity — Summer research project

12 weeks in 2018, University of Oxford — simulation using COMSOL

  • Published an information page and video on the website detailing my work to the general public
  • Worked autonomously when my supervisor left for a month to perform research abroad: completed the tasks she had set and used the results of those to guide the research
  • Worked closely with the team to develop simulations, meeting with them and presenting my work every fortnight

Perm State University — Computational fluid dynamics internship

6 weeks in 2017, Russia

  • Communicated effectively with a multi-cultural group of students and professors to maximise the value of the course on simulation techniques
  • Implemented and optimised these techniques in FORTRAN to achieve results comparable to papers in this field
  • Utilised their linux compute cluster to run simulations that I had parallelised myself using MPI

Education

UNSW — Doctor of Physics (2021–2026)

Thesis submitted, under examination

  • Primary supervisor: Prof. Michelle Simmons AC FRS

University of Oxford — Master of Physics (2015–2019)

1st class

  • Studied Quantum Information Processing, Theoretical Physics, and Lasers and Optics in my final year
  • Academic scholarships awarded for second and third year results

Wanstead High School — A levels and GCSEs

2013–2015: Maths A*, Further Maths A*, Physics A*, Chemistry A, Music A

GCSEs 2008–2013: 8 A*’s and 4 A’s including A* in Maths, Physics and English Literature

Skills

Numerical

  • Tensor network methods
  • Vectorised methods
  • Quantum simulation

ML & AI

  • Transformer architectures
  • Differentiable programming
  • Optimisation methods

Implementation

  • Python
  • PyTorch
  • Custom autograd

Publications

Patents