Computer Engineer
"Emerson was... an incredibly smart and hard working engineer, where there became no question from the people around him that he could handle difficult tasks."
"Emerson came in and hit the ground running. We're already trying to figure out a way to get him back here... I would hire him back in an instant."
I've founded and contributed to several projects. Gain insight into the work I've done across several engineering disciplines.
OpenBook is a daily fantasy (DFS) pick'em exchange app and platform currently in development. It is designed to offer users a pick'em experience with superior odds and investment tools compared to existing apps. I have personally built the majority of the backend and will be leading frontend implementation as the project progresses. This project has also been a great way for me to experiment with Claude Code and languages like Rust and Go.
View on GitHub →I spent six years at DRW, one of the world's leading proprietary trading firms. Over that time, I grew from a junior engineer learning the fundamentals of financial markets to a senior developer leading development for a team. While my work was primarily in C++, the experience gave me skills that apply across all areas of development: system design and architecture, problem solving, and speed of delivery.
My Capstone group developed a wearable navigational tool for the blind and visually impaired. I combined computer vision with machine learning to detect common signs (bathroom signs, exit signs, etc.) and determine their distance/angle relative to the wearer. View the code repository to gain insight into our development process.
View on GitHub →I developed an NFL match prediction algorithm for over two years with a team of four at Northeastern University's NUCAR. Our algorithm was able to predict 55% of matches correctly, greater than the 53% needed to profit. My role on the team was widespread, but I solely developed the dimensionality reduction and model selection tools. For dimensionality reduction, we used cross-validation in order to quickly determine the most significant features. In order to prevent overfitting with the models, we would use a validation set of data along with a training and test set.
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