Software Engineering · AI
I turn maths, data and ideas into software.
Building systems, simulations and models to understand problems, test ideas and make things work.
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Industry experiences
Insight & work experience programmes
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Major technical projects
Built from the ground up
A*A*AA
A Level Predicted Grades
Academics
A snapshot of my public development activity and the technologies I work with.
Systems I built to answer a question. Each one has a full case study covering the architecture, the maths, what broke, and what I would do differently.
A probabilistic race-strategy engine that simulates thousands of possible F1 races to understand which decisions remain strong when the race does not go to plan.
A distributed job scheduling system I built to understand what happens when queues, concurrent workers, retries and failures collide.
A real-time strategy game built from scratch in C# and MonoGame, with resource management, combat systems and an AI opponent.
An independent research project analysing 14,000+ hourly observations to investigate how weather conditions relate to air quality.
Insight programmes, work experience and research placements across engineering, software, data and finance. Expand an entry to see what it involved and what I took from it.
Independent statistical work, written up the way a report should be, including the parts that did not hold up.
Investigated the relationship between weather conditions and PM2.5 and PM10 using 14,000+ hourly observations, Python and statistical analysis. Produced a 32-page report, 16+ visualisations and earned a CREST Gold Award.
Is there a relationship between weather conditions and PM2.5 and PM10 concentrations?
Used 14,000+ hourly observations from a monitoring station, combining weather measurements with PM2.5 and PM10 readings. I cleaned and prepared the dataset before analysing the relationships, including dealing with missing values, unusual readings and variables with large amounts of missing data.
I started by researching how weather can affect air pollution and what previous studies had found. I then cleaned the dataset and explored the variables using Python before carrying out the main analysis.
I used Pearson correlation to measure the strength and direction of relationships between the weather variables and PM2.5 and PM10. I then used linear regression to investigate the relationships in more detail.
I created 16+ graphs and visualisations throughout the project to compare the variables and identify patterns that were not always obvious from the correlation results alone. I then compared the results with the scientific research and considered possible explanations for the patterns I found.
The main part of the project was working out what the results actually meant rather than just reporting the numbers.
PM2.5 and PM10 followed very similar patterns, while the weather variables showed different relationships with particulate pollution. I also looked closely at the unexpected precipitation probability result and separated this from actual rainfall rather than treating the two as the same thing.
I kept the conclusions focused on relationships within the dataset. Since the data came from one monitoring station and did not include factors such as traffic, heating emissions or humidity, I avoided claiming that the weather variables directly caused changes in pollution.
Courses and certifications I have worked through. Each one lists what I actually took from it rather than the syllabus, because the badge is not the point.
Communities, mentorship programmes and academic initiatives I have been involved with outside the classroom.
Grouped by what they are for, not scored out of ten. Hover or focus a skill to see where I actually used it.
How I got here
I'm a Year 13 student in Preston studying Maths, Computer Science and Statistics, and I spend most of my spare time building things that sit somewhere between software engineering and applied mathematics.
Most of what I know came from projects rather than lessons. I got interested in concurrency by writing a job scheduler that kept losing work when a worker died, and I got interested in probability because I wanted to know whether a Formula 1 team's pit stop call was actually the right one or just the one that happened to work.
Formula 1 is where a lot of that curiosity started - it's a sport where the interesting decisions are made under uncertainty with incomplete information, which turns out to be a good description of most engineering problems too.
I'm applying for Software Engineering and AI/ML degree apprenticeships so I can keep learning while working on systems that people actually depend on. Outside of that I play football and badminton.
I learn best by building. When I encounter something I don't understand, I try to turn it into a project, experiment or system I can test.
A LevelsSeptember 2025 - Present
GCSEs & Level 2 Engineering