Physics graduate with both masters and bachelors degrees.

My academic background is in quantum physics, with research experience in simulating superconducting qubits and developing neuromorphic systems.

In addition to this experience I have co-founded a legal AI startup and also previously worked as a data scientist, on both biomedical and financial applications.

I have a deep interest in understanding how intelligence emerges within the brain and how artificial systems can adopt such principles and architectures.

MSc Physics with Quantum Dynamics
Imperial College London
2021 - 22

Distinction

Advanced Classical Physics, Information Theory, Mathematical Methods for Physicists, Quantum Information, Quantum Information, Quantum Optics, Quantum Systems I, Quantum Systems II

BSc Physics
University of Nottingham
2016 - 19

1st class degree, 3rd in cohort

Applied Computational Engineering, Quantum Dynamics, Scientific computing, Soft Condensed Matter, Theoretical Elementary Particle Physics, Theory Toolbox

Simulating Superconducting Circuit for Charge-Basis Tomography
Imperial College London
2021 - 22

During my masters project I worked with Dr Malcolm Connolly and Dr Eran Ginossar to simulate a superconducting circuit, with the aim of enabling a future charge tomographic measurement on a superconducting qubit. This work fitted into a larger collaboration and is motivated by potential insights into the many-body physics of the Josephson Junction.

The Search for the Electron’s Electric Dipole Moment
Imperial College London
2021 - 22

As part of my masters I wrote a review of electron EDM (electric dipole moment) experiments. These experiments seek to measure a non-zero EDM, the magnitude of which would have major implications to the standard model, potentially providing experimental evidence of Beyond Standard Model (BSM) theories such as supersymmetry.

Classifying Phases of Matter with Machine Learning
University of Nottingham
2019

For my undergraduate thesis I simulated one and two dimensional Ising models and then trained both neural networks and convolutional neural networks (CNNs) to classify the phase of the simulated model. I found strong performance with both models (>97% classification accuracy), and as expected slightly superior performance with the CNNs. In this project I also developed my programming skills, for performance reasons I rewrote my matlab simulations in C and saw dramatic performance increases, enabling much larger model sizes.

Quantum Tunneling and Quantum Reflection
University of Nottingham
2019

As part of an undergraduate scientific computing module I developed simulations of a particle propagating in one and two dimensions. This particle was subjected to various potentials and tuning this potential resonances were observed, resulting in quantum tunnelling and reflection.

Co-Founder
Legal AI Startup
2023 - Present

Garfield is an Legal AI startup aimed at increasing access to justice by enabling unrepresented parties and smaller businesses to pursue small track claims. I have been developing the backend of this application within a small team. We're hoping to be able to share more soon!

Data Scientist
ElectronRx
2019 - 21

I previously worked as a data scientist at a Cambridge based biotech start-up. This role entailed developing predictive algorithms from signals captured by smartphones, as well as in-house developed devices. The primary focus was on developing an algorithm to infer blood pressure from a ppg (photoplethysmography) signal, taken by a smartphone camera. This involved harnessing approaches proposed in recent literature to develop a sufficiently accurate machine learning model and effective signal processing pipeline. The work was then integrated with the wider app platform (FoneDx), which aimed to allow any modern smartphone to become a medical grade device. In this position I also took significant responsibility in maintaining and developing the backend for the platform, as well as in the data engineering tasks of designing and implementing a data pipeline which used cloud computing services such as AWS S3 and AWS Lambda, allowing us to process terabytes of biomedical data.

Data Science Intern
Featurespace
2019

Over a two-month period I worked to produce an account application fraud detection model. The model was trained to score applications as either fraudulent or safe. The model had many components, including features generated from user sequence analysis, computed using Markov chains, an entity resolution component and a more general network analysis components. The features were then passed to an XGBoost classifier which achieved an AUC of over 0.9.

Technical Lead
CleanPlate
2021 - Present

In my spare time I have been working with friends on a non-profit pushing for carbon labelling on food packaging. To push for this we are developing a mobile app which enables food influencers to easily label their recipes and share insights into the carbon impacts of the ingredients with their audiences. By enabling these individuals we believe we can reach a large audience and help inform their dietary choices. The app is currently in a final user testing phase before release, we have also successfully been awarded several grants, which will enable the promotion of the app.