At IBM I designed and implemented an ETL process. This involved writing a design document outlining the requirements, specifications, and use cases of the customer, and relaying this information to an offshore team. We implemented the process using Automation Anywhere. I also had a neat experience working with the Skydio R1 drone, where, using OpenCV, Azure, and Power BI, I set up a Proof-Of-Concept warehouse inventory management system.January 2019
XE served as my introduction to production Node and React code. I spent a few months working on a shopify currency converter, which allows for online stores to seamlessly display prices to international customers. This opportunity allowed for me to work with a great team of designers, product managers, and developers. I was also introduced to AWS and Alexa Skills, where I worked on creating a skill that provides historic exchange rates.May 2018
In high school I tutored students in Unity, PHP, and SQL. I also ran prep sessions for the CCC (Canadian Computing Competition), where we discussed algorithms and problem-solving strategies that are prominent in competitive programming. I enjoy learning through case competitions and hackathons, and have competed in over 15 collectively to date.
DevLink is a social media platform that allows developers to connect, create profiles, post, and comment. The backend was built on Node.js, using Express for routing, Passport for authentication, and a MongoDB database. The frontend was then built using React, Redux, and bootstrap. I also gained experience with deploying through Heroku.
I worked with a team of four at Hack the North to develop an Android application that enables users to more easily manage their budget.The app is proof-of-concept and works with the TD Da Vinci API to simulate virtual users, as well as the Yelp Fusion API to make recommendations on shopping destinations that better suit customers' budgets.
I created this tool at Hack the North 2019, to test if current exchange rates show any relationship to the tonality of financial news articles written in the country or countries that the currency belongs to. Written in Python, News APIs were used to aggregate news articles, using a search parameter relating to country of origin. Articles were then filtered down to financial and business news, using Google's Natural Language Processing API. Sentiment analysis was conducted on the remaining articles, assigning each article a score. Scores were aggregated by date, and the difference of the scores between two countries was taken, and compared to the historic exchange rate. Unfortunately, no obvious trends were present, and a rolling means regression conducted using Pandas did not prove to be of value. Plotly was used to graph the relationship and provide a visualization.