I develop machine learning frameworks for modeling complex, structured data. My current focus is on synthetic financial time series — generating realistic, scenario-aware datasets that support risk modeling, stress testing, and portfolio analysis.

I’m the founder of Skanalytix, where we apply the UNCRi framework (Unified Numeric/Categorical Representation and Inference) to create practical, interpretable, and data-efficient tools for finance. My goal is to make advanced ML techniques both usable and reliable in high-stakes, real-world settings.