My romance with coding began a few years ago when I started studying Computer Science at Thompson Rivers University. Now, I'm in the honeymoon phase with software development. I'm a huge fan of how it can take me to places I’ve never been before and enable me to do things I could never achieve without it.

At the moment, I’m excitedly looking for opportunities in the development world as a junior software developer. If you know someone who knows someone, you have the chance to help me solidify my bond with programming and let us live happily ever after in the world of code.

Just in case you don’t, I’m looking to collaborate on JavaEE, Spring, or Python projects. Don’t be shy—feel free to drop me a message on LinkedIn!

Here are the major recent projects. Dive into the repositories for more visuals and in-depth info. Trust me, they're mint👌

To make website data extraction and analysis more efficient, I developed a tool using Java and JSF for the interface, Jsoup for data extraction, and JPA for storage.

• Developed an intuitive web interface with Java and JSF that allows easy URL input and parameter setting.
• Automated the retrieval of phone numbers, emails, images, and PDFs using Jsoup.
• Organized and stored the extracted data using JPA for clear presentation and future reference.

For the purpose of assisting players with manual life point recording and game management, I developed an Android app that simplifies these tasks for Magic: The Gathering.

• Built a user-friendly platform using Android Studio and Java for creating accounts, tracking life points for up to 4 players, and managing game states.
• Implemented features such as dice rolling, game saving/loading, and random card introductions using SQLite for reliable data storage and Glide for image loading.
• Designed intuitive navigation and customization options to enhance user experience and engagement.

Developed a Python-based algorithm to make more informed and data-driven trading decisions.

• Developed an algorithm using the Monte Carlo method
• Implemented a Monte Carlo approach to forecast prices and execute trades based on simulated data, iterating 10,000 times to generate a matrix of projected prices.
• Refined the initial static model to improve predictive accuracy, achieving a 7% yearly return compared to the initial 2% return, while maintaining a 0.02% risk level through manual stop-loss.

Here are the school projects and experiments.

Conducted a performance comparison between Java and Python to evaluate how each language handles data analysis tasks using the NASA API.

• Benchmarked various techniques including JSON parsing, regex extraction, and multithreaded processing to measure execution time and efficiency.
• Analyzed performance differences with and without external libraries to determine the impact on processing speed.

Website developed for a web development course.

Fishing script for an online game.