The Engineer-Physicist

From Data Engineering to Market Physics

For years, I worked as a Data Engineer. I optimized massive pipelines (BigQuery/Snowflake/Airflow) and built real-time dashboards (React). But I kept running into the same problem: the Model Gap.

We were engineering pristine, high-quality data, but feeding it into "Newtonian" financial models that assumed markets were stable. I realized that to diagnose fragility I needed more than just a dashboard. I needed a simulation.

I built the computational foundation of this Lab to apply Dissipative Systems Theory to financial microstructure. My engineering background isn't separate from my research; it is the prerequisite. You cannot calculate the Instability Index (Λ) without efficient architecture.

Reproducible Science requires Calm Systems

Complexity belongs in the thermodynamic equations, not the infrastructure. Our research is built on a "calm architecture" philosophy that ensures reproducibility and cost-efficiency:

• Reliability is Evidence: If the pipeline breaks at 2 AM, the data is corrupted, and the science is invalid. I prioritize idempotency and self-healing code to ensure data integrity.
• FinOps is Grant Management: R&D burns cash. I design for extreme efficiency (e.g., local vectorization) to ensure our grant funding goes to science, not cloud overhead.
• Security is Foundational: Proprietary IP requires private-by-default networks. Zero trust isn't a buzzword; it's how we protect Protocol #1.

The Research Stack

I don't just theorize about these tools; I use them to power the CatInCloud Simulation and for the blueprint of our backtesting engine.