During 2014 – 2017 I studied computer science at University College London. Below are some of the projects I worked on during my studies.
Room bookings
July 2017 • Source code • Website
I had a long obsession with room bookings at my university – I had built a room bookings scraper two years before this project. At the end of my studies an API got released, so I created this quick tool to:
- Quickly see availability across the entire campus.
- Search for available time slots with flexible timing.
This was intended to solve a problem we often had while running the technology society – we were flexible around the timing of our events as long as we could find a good room. But the university’s room booking tool required entering start and end times before it showed what is available.
End of year course results
June 2017 • Source code • Visualization
Every year the department published everyone’s marks for each course as an Excel file. I built this visualisation to put everyone’s marks in context.
See also the 2016 and 2015 versions.
Satellite trajectory prediction
February 2017 • Source code • Visualization
I took an Aerospace Engineering module where we had to write a program that predicts the trajectory of a satellite given some starting conditions (position and velocity). We were taught a few different methods, I built a visualisation to compare them.
See the visualisation which contains more information.
UCL Technology Society website
June 2016 • Source code • Preview
From October 2015 until June 2016, I was the society’s “webmaster” and I built and maintained the website.
I built the website using Jekyll and deployed it on GitHub Pages. My favourite features were the ones that were hacked together and uncommon for Jekyll sites, like: calendar subscriptions, JSON feeds, Facebook event synchronization.
Matching physical trains to train schedules
April 2016 • Source code • Report • Visualization
This was a university project with an external client.
In a team with Api and Axel, we developed a spatiotemporal matching algorithm to match physical trains (tracked via GPS) with scheduled train services, using real-time location data from train operators and timetable data from Network Rail. The solution helps detect when trains are running off schedule, are diverted, or are facing delays.
An example “matching output” can be seen in the visualisation.