Architecting proprietary intelligence to quantify and maximize strategic advantage in high-stakes environments
Standard models fail in the face of real-world complexity. The intricate, emergent dynamics of financial markets, supply chains, and large-scale operations cannot be captured by simple regressions or off-the-shelf algorithms. Lasting strategic advantage is not found; it is architected.
My work is centered on this principle. I build high-fidelity "Digital Twins"—statistically-grounded simulations of complex systems—that serve as virtual laboratories for strategy. By fusing deep research in Agent-Based Modeling and AI with practical, high-stakes application, I create systems that allow decision-makers to quantify their advantage, test counterfactuals, and optimize choices before committing capital.
Models Must Mirror Reality
To be useful, a model must capture the emergent behaviors of the system it represents. My approach, grounded in Agent-Based Modeling and causal inference, respects this complexity. The goal is to build frameworks that are not just predictive, but explanatory, revealing the mechanisms that drive outcomes.
Intelligence Creates Durable Advantage
Proprietary data is a temporary edge; proprietary intelligence is a durable moat. I architect systems that learn and compound, transforming an organization's unique operational expertise into a systematic, defensible advantage. This is about building an engine for insight that competitors cannot easily replicate.
R&D Must Drive Quantifiable ROI
Academic rigor is the foundation, but commercial impact is the metric of success. I focus on translating theoretical advances into deployable, enterprise-grade systems that produce measurable results—whether it's a 15x performance boost in a statistical engine or a +25% uplift in business effectiveness.
My work spans the full spectrum from foundational research to commercial productization. These examples illustrate the journey from a theoretical insight to a strategic asset.
Cutting-edge statistical methods often remain confined to academic papers due to computational costs. A core part of my work involves implementing and re-engineering these techniques for production environments. This includes developing highly-optimized Python libraries that deliver 5-15x performance gains over standard implementations, enabling the use of statistically rigorous tools like conformal prediction in real-time financial applications.
Building on optimized libraries, I architect comprehensive frameworks to monitor the "advantage function" of investment or operational strategies in real-time. Crucially, these systems are not just for monitoring; they are for simulation. They allow for rigorous counterfactual analysis—"what if we had acted differently?"—which is essential for optimizing policies and understanding the true drivers of performance.
The final stage is to abstract this immense complexity into an intuitive, value-driven product. HotelCrema is a "digital twin" of hotel operations, built on a sophisticated ABM engine. Its front-end guides non-technical users (e.g., hotel managers) through a process to define their goals and constraints, which then configures a bespoke simulation to find optimal operational policies. This translates a complex AI system into a tool that directly addresses business KPIs like cost, efficiency, and guest satisfaction.
My approach is built on a versatile toolkit designed to solve complex problems and create robust, scalable intelligence systems.