I run the the online game room store, Game Room Hub.

Upon resigning from Moment AI, I started an online retail business leveraging Amazon’s marketplace to sell branded products. From this position I researched the Amazon marketplace to identify my target customers; I contacted suppliers in China to manufacture my selected product; I hired freelance workers to create and design a brand; I communicated with suppliers to ship my product to the U.S. I leveraged Amazon’s warehouses to store my inventory; I then marketed my product by running multiple ad campaigns to interested buyers. In this position I have sharpened my problem solving and critical thinking skills through direct experience.

Upon graduation from university, I co-founded an artificial intelligence company, Moment AI, Inc. and acted as the sole software engineer and chief technology officer. I designed and developed the first few prototypes of the company’s product, a safety system for monitoring drivers and responding to sudden medical events, such as seizures. The success of the prototypes allowed the opportunity to hire a full development team. As a team, we evolved the prototypes into two functioning products: a cloud deployed version, leveraging AWS to enable remote video processing; and a native mobile app version, for android. In parallel with development, I was able to effectively communicate with investors and business leaders about the intricacies of the product.

The summer before my senior year at Rhodes College, I attended and graduated from the extremely rigorous United States Marine Corps Officer Candidate School, as a part of Class 228. I was there for 10 weeks of leadership training and uncomfortable situations, created to test a person's ability to confidently lead Marines. Following graduation, I decided to decline my U.S. Marine Officer Contract in order to pursue my startup company.

While attending Rhodes College, I had the opportunity to work in a Pediatric Biomedical Informatics Lab under Dr. Rishi Kamaleswaran as a research intern. Our work focused on the development of PhysOnline, a online pipeline built to ingest streaming physiological data for online feature extraction and machine learning. To validate our work, we analyzed electrocardiography (ECG) recordings to screen for Paroxysmal Atrial Fibrillation (PAF), a dangerous heart condition, and demonstrated that our pipeline can predict persons developing PAF at least 45 minutes before an episode of that condition. My specific role was managing the large amounts of data needed to train the A.I. and I was a co-author of the paper we published.