In computer science, we mainly learn Java and a bit of HTML and CSS for our websites (one of which you are currently on!). Ms. Taricco teaches this course. Most of our assignments are done in Eclipse, which is a programming software. We have a lot of assignments focused on either Graph Art (which is really cool) or ways to use computer science in real-world scenarios, such as using it to see if a credit card number is valid.
Above is the code and output for our AWT Graphics assignment where we had to recreate a bunch of shapes and figures using Java. Graphics is a really fun application for Java!
Above is also the code and output for another Graphics assignment, Line Art, where we had to use lines to simulate curves in the shape above. It was a fun assignment and an interesting way to make curves.
I was in a team with Adnan Dembele, and Ethan Zhou, and together we made Spark. Spark’s mission was to create an app that focused on helping runners and athletes find the optimal playlist to listen to while doing their workout. We created the app TuneRunner to do so. Everyone enjoys listening to music while working out, and so we wanted to make sure athletes are doing so in a way that boosts them while their working out. Listening to music that does not match your BPM while you are running can make it so that you either slow down during a pivotal moment in your workout or go too fast when you really should be conserving energy. An important aspect of distance running is pace (I would know) and so we wanted to ensure that our users were keeping pace while also pushing them to do better and have more endurance. This app specifically focuses on runners, however, it can also be adapted to other sports such as ice skating, cycling, and swimming.
TuneRunner solves this problem by creating a playlist based on the athletes' running pattern and overall pace and distance throughout their workout. Our MVP was creating graphs that were able to demonstrate the different running patterns and being able to match music to the predicted BPMs that users would be running at during different times in their run based on the running patterns that we created. The user would select from 4 different graphs (time vs. speed), all of which display different common running patterns that has been recommended or seen by coaches and used by athletes. Then our app would bring them to a different screen in which they would be able to adjust the graph with their average running speed and distance.
Our algorithm would adjust the slope and distance of the graph respectively, as well as projecting the total time of the work out. All of this data would be used to project the user’s BPM during their run. We then matched a list of songs and their BPM’s to the projected BPM’s of the runner, creating a playlist that the user could play in the app.
This was overall an amazing and fun project to work on and im happy to have worked with Adnan and Ethan while doing so.