Quantitative Researcher | Bayesian Statistics | Financial Markets

Core Expertise:

• Bayesian methods for financial markets with focus on uncertainty quantification and probabilistic inference
• Sequential learning algorithms to detect regime changes and structural shifts in time-series data
• Signal generation, enhancement and combination methodologies via real-time Bayesian inference and online learning algorithms

I am quantitative researcher at Brevan Howard, where I develop statistical frameworks for signal generation in global macro markets. I have experience building and deploying Bayesian models that help navigate regime changes and market transitions.

I hold a PhD in Statistics from the London School of Economics, where I specialized in Sequential Bayesian Learning for State Space Models under the guidance of Kostas Kalogeropoulos and Pauline Barrieu. My doctoral research centered on developing methodologies for parameter estimation in dynamic systems, with particular emphasis on latent variable models and time-varying environments.

I am happy to engage with fellow quantitative professionals and researchers exploring innovative applications in financial markets.