I have successfully delivered multiple complex projects in various industries. These projects involved:

• Deploying machine learning infrastructure within a tightly regulated environment, processing highly confidential information, while working closely with stakeholders across cloud infrastructure, security, and the data science teams to ensure a good outcome. This project has also been recognized for pioneering the deployment of machine learning infrastructure within KPMG globally.
• Implementing a data analytics platform from scratch, using Azure and Snowflake, and integrating CI/CD workflows in Azure DevOps to automate deployment of documentation and Bicep infrastructure-as-code. This project included implementing two model data pipelines as starting points for the client, taking Excel and API data through to a curated layer.
• Conducting a thorough review of a university's Integration Capabilities Uplift program's platform, with a focus on the business need, clarity and scope driving the implementation, the capabilities being provided by the platform, and the suitability of the technology design. Our team provided the client with insightful recommendations as to the program's strengths and areas of improvement.
• Acting as the Technology Lead and Scrum Master for a foundational and modular Azure Data Platform accelerator and a configuration-driven orchestrator framework for Data Factory, building upon the former accelerator. I also developed a modular and extensible Python framework to facilitate the deployment, configuration, and development of the Azure Data Platform accelerator.

Overall, I have a strong track record of delivering innovative and effective technology solutions while collaborating closely with stakeholders to ensure successful outcomes.

Conducted research on an E6-inspired seesaw neutrino mass model under the guidance of Prof. Volkas, exploring the relevant parameter space through the generation and visualization of large data sets.

Developed a new computational tool to solve highly coupled Boltzmann differential equations involving hundreds of terms and rates spanning multiple orders of magnitude. This tool allows for the calculation of the evolution of particle densities and asymmetries in the early universe, and the resulting matter--anti-matter asymmetry. This was developed in Rust and featured novel integration quadratures to efficiently evaluate certain integrals numerically, and required extensive analysis and optimization of numerical algorithms to ensure results were both accurate and computable within a reasonable timeframe.

Utilized the above-mentioned computational tool to investigate the validity of common simplifying assumptions and explore previously difficult-to-reach parameter space in two-Higgs doublet models.

Investigated the phenomenology of an E₆-inspired seesaw neutrino mass model under the guidance of Prof. Volkas. Explored the relevant parameter space through the generation and visualization of large data sets and constrained the parameter space using experimental data from the LHC and cosmological observations.

Completed coursework achieving a 1^st class honours average.

In my spare time, created TikZ-Feynman, a LaTeX package for drawing Feynman diagrams. This has become widely adopted by the particle physics community, with over 400 citations.

Completed a major in physics and concurrent diploma in mathematical science with a focus on pure mathematics. Completed coursework achieving a 1^st class honours average in both and was a recipient of the Dean's List Award for my performance.

Attended the CERN summer school in 2013 where I helped developed a level 1 trigger for the LHC in C++ and ROOT.