I work in quantitative risk at Charles Schwab focusing on crypto currency and machine learning. Outside of work, the problems I enjoy most sit across credit, rates, FX, and quant development: building systems that price, hedge, and explain what changes when markets move. Before that I completed a PhD in statistics at the University of York. Across all of it, the focus has been on models, code, and financial systems that remain useful when the inputs are messy and the assumptions need to hold up in production.
Quantitative Risk in Digital Assets at Charles Schwab.
My work centers on understanding risk, model behavior, and failure modes in digital asset workflows. I work with both classical risk models and newer machine learning approaches, with an emphasis on keeping the models stable as market conditions and data sources change.
The thesis, paper, and projects pages are the best places to see the work in public.
Forecast aggregation, prediction markets, market microstructure, and sequential decision problems where the environment changes over time. I also spend a lot of time thinking about fixed income hedging, MBS, and duration risk in rate environments that move quickly.
I have taught probability, statistics, linear models, and practical data analysis modules across multiple academic years. The common thread is turning ideas into something that actually works.
The thesis and paper pages are the best places to see the kind of work I like doing in public.