Bridging the gap between academic theory and real-world strategy.
Traditional economic models assume equilibrium. But real markets are dynamic, adaptive, and constantly evolving. Complexity Insights applies cutting-edge computational methods to understand these systems as they actually are—complex, interconnected networks where small changes can cascade into large effects.
Mapping financial contagion pathways and identifying "Too Connected To Fail" institutions.
Simulating emergent behavior to test strategies in risk-free synthetic environments.
Moving beyond linear regression to capture non-linear dynamics and feedback loops.
Quantifying tail risks that standard VaR (Value at Risk) models systematically ignore.
Applying network theory to deal sourcing, synergy modeling, and post-merger integration risks.
Bridging the gap between complexity science and traditional valuation, capital allocation, and FP&A.
I am an economics graduate and researcher focused on the intersection of complexity science and financial markets. My work challenges the static assumptions of traditional economics by applying computational methods to real-world data.
During my studies, I became frustrated with the gap between textbook models and market reality. The 2008 financial crisis wasn't just a "black swan"—it was a failure of models that assumed independence in a deeply interconnected system.
I founded Complexity Insights to bridge that gap. This firm serves as both a consultancy and a research laboratory, applying Agent-Based Modeling (ABM) and Network Theory to solve strategic problems in finance and risk management.
I am currently available for full-time roles in Economic Research, Data Analysis, and Financial Modeling, where I can apply these rigorous quantitative methods to drive business value.
Interested in collaborating, hiring, or commissioning research?