• Implemented machine learning models to analyze market data and identify patterns, leading to more accurate and informed trading decisions, resulting in up to a ~35% increase in profit on back-test data.

• Reconfigured existing .Net and Java code base to be compatible with each other on gRPC services.

• Led proof of concept projects, demonstrating the company's AI software capabilities to clients by leading meetings with the client, managing the most optimal direction to take the project, and producing improvements on their models.

• Completed a project for one of the largest healthcare companies, based off of their pre-existing Covid-19 survivability predictor, and had a 7% improvement the AUROC metric while maintaining the same performance on the ICI metric, decreasing the false positive rate.

• Created and published an open source GPU-Based Elastic Net Regression library in .NET using ILGPU, which decreased runtime by up to ~83%.

• Created multiple other GPU-based libraries using ILGPU from scratch to improve program runtimes. (Stochastic Gradient Descent, Mutual Information Algortihm, Benford's Law Algorthim)

• Used NLTK sentiment analysis of Tweets from commodity traders to create machine learning models that predicted oil prices with over 70% accuracy.

• Researched upcoming technologies to see if they were applicable to implement improvements on the software