Faramarz Jabbarvaziri

Faramarz Jabbarvaziri is a senior data scientist who builds and evaluates production machine learning and LLM agent systems. He holds a PhD from the University of British Columbia, where he researched fast model adaptation strategies for deep learning in telco and 5G systems operating in dynamic environments. He has since shipped machine learning into settings where being wrong is expensive — regulated financial services and adversarial fraud detection — and has built agentic systems for long-horizon tasks, where the questions are structural: how an agent should be organized, and what determines whether it can be run at scale without the cost getting out of hand.