About

I am a quantitative researcher focused on the application of statistical models and machine learning techniques to financial markets. My work spans the intersection of applied mathematics, computer science, and financial economics.

My current research explores the use of Hidden Markov Models for detecting market regimes in cryptocurrency markets — specifically developing causal, forward-filtering approaches that prevent lookahead bias and can be deployed in live trading environments.

I am committed to rigorous empirical methodology: every claim is backed by out-of-sample validation, proper benchmark comparison, and transparent reporting of both successes and limitations. Research that cannot be reproduced is not research.

Beyond the technical, I am interested in how quantitative methods can be made more accessible — bridging the gap between academic finance and practical implementation.