Ok, sure.

I was on track for a double major in mathematics and computer science at a Canadian university.

I dropped out 2 years in, after I secured an R&D internship under the chief architect of a 25,000 employee bank tech company. I would build PoCs and communicate architecture decisions with VPs and Team Leads through demos and presentations.

After that I joined an insurance tech startup as the director of Analytics. I led a team of 2-4 building out various projects — the most notable of which is an underwriting platform for cyber insurance. I focused on the underwriting engine, which involved cost and risk assessment models. I implemented ensemble classifiers, Monte Carlo method, clustering, data mining, etc.

I then launched my own firm, focused on machine learning and blockchain (smart contracts). I work on a variety of projects, from data analysis for a class action lawsuit to product features involving distributed sentence Embeddings. I am also working on my own research, involving information-theoretic objective functions, inspired by a paper titled "causal entropic forces" from a few years back.

A lot of people seem to hate the idea that autodidacts are able to reach or surpass the competency expected of those who taught in academic environments. I like to refer these people to the story of Srinivasa Ramanujan.

This is not to say that I compare myself to him in any capacity outside of simple admiration for his story. I just want to point out that these limits we impose on ourselves are self fulfilling. Whether you believe you can, or you can't —you are right.