Final-year Data & Analytical Science student in Peterborough, England, dedicated to finding clarity in complexity.
Hello, world — welcome to my website!
I have practical experience implementing a wide array of models — random forests, gradient boosting, regression and support vector machines — and managing data in SQL and NoSQL (MongoDB) environments. With further experience in full-stack web development (React/Node.js) and backend systems (C#), I look to bridge the gap between robust software engineering and deep data insights.
I am passionate about leveraging data to drive informed decision-making, and I am actively seeking graduate opportunities in data science where I can apply my skills to real-world problems.
A near-real-time anomaly detection pipeline for English freshwater pollution events, fusing regulatory, sensor and citizen-reported data streams.
A research-grade platform for ACMG/AMP-aligned genetic variant interpretation with per-criterion conformal abstention, multi-agent orchestration and full evidence provenance.
A globally scalable, research-grade solar analytics and digital-twin platform combining multi-source environmental data, physics-based PV modelling and machine learning.
A multi-source stacked ensemble predicting footballer market values from Transfermarkt, FBref and EA Sports FC data — R² 0.958, MAE under €1M.
My goal is to build a strong applied AI and data science portfolio focused on real-world scientific, environmental and industrial problems — spanning machine learning, data engineering, forecasting, geospatial analytics, AI agents and research-grade software development, with an emphasis on work that is technically rigorous, useful and clearly explainable.
Alongside the projects, I am strengthening the domain-specific mathematics and statistics required for advanced data science — documented publicly in my study logs on GitHub.