Master of Science in Computer Engineering
After two years at Capgemini, I knew traditional backend work had run its course. I wanted to understand AI properly. Not just use the APIs, but actually get how the systems work under the hood.
NYU Tandon was a hard reset. Two years of machine learning, neural networks, and all the math that makes modern AI tick. I wasn't there for the degree. I was there to learn how to build things I couldn't build before.
“The best engineers aren't specialists. They understand systems deeply enough to connect things others can't see.”
Going from backend to AI wasn't starting over. It was adding a new layer. The same instincts that helped me build reliable systems translated directly to building AI pipelines that actually work in production. Tandon gave me the theory. The real learning came from building.
Python framework for ML workflows. Data prep, hyperparameter tuning, and model comparison in one pipeline. Built for people who want to experiment fast without boilerplate.
Algorithmic generation of aesthetic chess puzzles. Optimized for beauty and counter-intuitive solutions, not just difficulty.