Different systems. The same curiosity.
Biology generates massive amounts of data. The challenge isn't just collecting it; it's knowing what it means and what to do next.
I came to biological modeling through chemical engineering and process control. The systems changed, but the mindset remained: map the interactions, test the assumptions, and design a useful intervention.
Today, I build the AI and computational frameworks that act as the bridge between raw biological data and engineered solutions. I run virtual experiments to decode the system, turning wet-lab data into actionable, predictive blueprints.