I am not learning AI just for theory.
I am learning it to build better systems.
That is how I am approaching the Break Through Tech AI fellowship in partnership with Cornell Tech. The coursework, labs, mentorship, and industry projects matter because they connect directly to the products I am already building.
My mindset
Ship → learn → improve → scale.
That is the loop.
Every AI concept becomes more valuable when it can improve a real product: a better recommendation flow, a stronger evaluation process, a clearer user experience, or a more reliable decision-support system.
Why it connects to PathWise
PathWise depends on applied AI being useful, safe, measurable, and understandable. Students and advisors need outputs they can trust. That means learning AI is not just about models. It is about evaluation, responsible AI, stakeholder communication, and product design.
What I want from the fellowship
I want to sharpen:
- machine learning fundamentals
- model evaluation
- responsible AI thinking
- applied project execution
- stakeholder collaboration
- product translation
The goal is to bring that learning back into the systems I am building.
The builder’s standard
A credential is useful. A product that works is better.
The best outcome is both.