Background[edit | edit source]
Julia is a fourth year student at Western University completing a dual degree in Honours Specialization Computer Science and Honours Business Administration (HBA) at Ivey Business School. Her academic interests include back-end engineering, web design, distributed ledgers, and big data.
She previously interned in technology consulting at Deloitte and is currently looking for a software engineering role for Summer 2023.
LinkedIn | Github | Personal Website
Projects[edit | edit source]
Smart home hub using Raspberry Pi[edit | edit source]
For our CS3307 project, each team selected their own idea for a program that used a Raspberry PI computer and developing using the C++ language. Our team is creating a smart home hub which consists of a screen that shows a person's recent emails, upcoming calendar events, the time of the next bus from the user's nearest bus stop, and the current weather.
Julia's role is to build the bus feature, which involved parsing CSV files from London's Open Database and developing logic to find the common route between two bus stops. The next step in this project is to connect the features together through the UI.
COVID-19 vaccine authentication on open-source ledger[edit | edit source]
Github repository | IOTA Explore open-source ledger
This prototype program takes a user's vaccination records from command-line input and puts the information on the IOTA Explore distributed ledger. The distributed ledger is used to authenticate the user's vaccine information upon vaccination and make them accessible to any other establishments. This project has a Rust back-end, which made development challenging since the team only had experience with the C programming language.
This project was developed in a 24-hour hackathon (RU Hackathon) and won Best Distributed Ledger Hack sponsored by Zebra Technologies. Julia's role was to develop the functionality that generated command-line prompts, parsed the user input, and stored it in a data structure to be sent to the distributed ledger.
Walking-trail creation web app using London's Open Database[edit | edit source]
In a 48-hour hackathon (HackWestern 7), our team created a mock Flask web app that generated walking-trails for users based on their preferences of what they wanted to see on their walk (public art, trails, trees) and distance. The app parsed CSV files from London's open database for coordinates of these attractions and used a depth-first search algorithm to find the appropriate coordinates in the specified distance. Julia's role was to develop the Flask framework and connect the front-end HTML/CSS/JS with the Python back-end.
Our project won the Best Machine Learning Hack award sponsored by the City of London. In addition, the City of London AI team wanted to work with our team to complete the development of the app for public use by residents in London. For several months, we worked with the AI team to prepare a SOW and plan the improvements we were going to implement. Unfortunately, the city did not have enough funding to support us in the project.