I build production-grade AI systems that connect rigorous statistical science with measurable commercial outcomes. My work sits at the intersection of causal inference, agentic AI architecture, and large-scale ML. I designing platforms that move organizations from manual, rules-based workflows to automated, probabilistic decision-making.
As VP & Partner of Data Science at Known, lead development of our Skeptic™ platform — an agentic AI system that orchestrates multi-step decisioning across Fortune 500 enterprises, built on MCP and A2A protocols for interoperability across complex client infrastructure. The platform has delivered $500M+ in incremental ROI across 65+ production applications.
I also teach at Columbia University, where I supervise graduate research and run courses on data visualization and programming — keeping one foot in academia while building in production.
- 🔬 Research focus: Causal inference, experimental design, and contextual bandits applied to commercial decisioning
- 🏗️ Building: Agentic AI orchestration systems with MCP / A2A protocols
- 🧑🏫 Teaching: Data Visualization & Programming at Columbia University
- 🔗 Connect on LinkedIn



