Second-year Computer Science student at Brunel University London, predicted a first. Open to placement, internship and graduate roles in software, data science and AI.
Most of what I build involves real data, real hardware, or systems that actually do something. I like pulling things apart to understand why they work.
- Running LLMs locally through Ollama to see where they hold up
- Building and benchmarking data and AI pipelines in Python
| Area | Tools |
|---|---|
| Languages |     |
| Data & ML |     |
| Platforms |      |
SwiftBot — Java and Raspberry Pi robot that reads traffic light colours with computer vision and drives itself accordingly.
Email Calendar Evaluation Pipeline — Benchmarks rule-based and LLM extraction of events and action items from 1,000 Enron emails, scored on precision, recall and F1.
Message Prioritisation Benchmark — Rule-based, ML and LLM approaches compared for priority classification, including the ambiguous and edge cases.
Skybound — Unity open-world RPG in C# with persistent saves, combat and enemy AI built on finite state machines.
- Finalist at the Eli Lilly x MSD Health Equity Hackathon 2026
- 1st Place and Community Favourite at the BrickLife London house price data hackathon
- Brunel Best Award at the Hillingdon Council hackathon, for a waste route optimiser that fed into their planning
- AWS Certified AI Practitioner, AWS Certified Cloud Practitioner, Databricks Fundamentals
I like structured code, clear documentation, and understanding why something works rather than just that it works. If a project here doesn't have a README, I probably haven't pushed it yet.
