Building a neural network using just NumPy: Summer 2020
As I discovered my passion for Machine Learning (and more specifically, Neural Networks) I learned that the way most people approach the subject is to use Tensorflow or Keras to create a powerful network to predict something. While I'm interested in the performance and simplicity of using tools like this, I wanted to get a deeper understanding of how a neural network really works. I found this awesome book that teaches you how to build neural nets in Python using just NumPy. I spent time diligently reading this book, discussing implementations with peers, and developing my code. At any rate, I implemented and fully understand just about every single line of code. From optimizers, to backpropogation, its all in NumPy! Feel free to reach out if you have any questions. Also I used this to develop a model to predict significant coronary disease in patients, using a cardiac catheterization dataset. I achieved a prediction accuracy of ~75% for a binary classification task. (Github link for this project).

ProjectX: March-November 2020
I worked with a team at UofT AI to design, schedule, and prepare a 3 month long machine learning research competition aimed at incentivizing undergraduates to get involved in ML research. I worked on this project essentially from inception (in February-March of 2020) to execution (Sept-Nov of 2020). While my role was technically 'External Relations Executive', I wore many hats. Early in the process I worked with 2 other students to reach out to dozens of universities and colleges across North America to field teams. This was especially difficult as the Covid pandemic was just getting into full swing, so CS departments at these institutions were preoccupied with going online. However, we were eventually able to register over 20 teams (including teams from MIT, Stanford, Carnegie Mellon, and UC Berkley to name a few). I also worked to find folks in academia and industry who would donate their time to guide the teams, and as a result I secured nearly all of the (UofT AI provided) mentors for this competition. At the end of the summer, I was given a new role: 'Director of Logistics'. In this role I helped lead the charge in interfacing with teams (particularly answering their questions throughout the competition) and ensuring submissions were submitted, anonymized, and assessed by the judges. You can see our website here.
We were also featured on The Strand, The 'Everything Under the Sun' Podcast, as well as by the University of Toronto itself.

Smaller Projects

Building a website
I made a mediocre website using html/css and bootstrap.