Borna Ardehi is a pre-doctoral graduate researcher in the PINPOINT project. His research examines shocks and perturbations in global supply chain systems, with a focus on supply chain security, trade war conditions, and sanction-sensitive trade networks. He works with large-scale shipment datasets and uses quantitative forecasting models (HMM/EBMA), text-as-data methods, and qualitative foresight scenarios to study patterns of relabelling, trade relations, and sanction evasion.

His current work applies Markov modelling to detect behavioural patterns in global supply chains under conditions of geopolitical and regulatory pressure. He holds an MA in Political Science from HSE University St Petersburg and is completing an MA in Political Science at LMU Munich. His methodological training includes predictive modelling, statistical modelling, text-as-data methods, machine learning, natural language processing, and structural causal modelling.

In the PINPOINT project, Borna focuses on firm-level trade patterns, compiling a large-scale data set of production networks, and network modeling.