My interest in crypto goes back to 2015, when I started following the space purely as a student, reading whitepapers and trying to understand what made this asset class different. In 2019 I moved from studying to doing: investing small amounts, learning how liquidity pools and AMMs actually behave from the inside rather than from a paper. Over time that curiosity pulled me toward less liquid markets specifically, the places where pricing is hardest and most of the interesting risk actually lives.
That pull is what led to my MSc thesis: a production-grade rBergomi calibration engine for Bitcoin inverse options on Deribit, built from scratch in Python and stress-tested against seven of crypto's worst days (LUNA, FTX, SVB), cutting RMSE by 45% and calibration time by 20x on 57,000 raw market files.
What makes me great is that combination: nine years of genuine, money-on-the-line intuition for how crypto markets actually move, paired with the quantitative rigor to turn that intuition into models that hold up under stress. I don't build toy models or trade on vibes. I want to build the pricing and risk infrastructure for a market that's still writing its own rules, and I'd rather be early and rigorous than late and comfortable.