Hernan Picatto examines supply chain interactions at the firm level using web data. His research includes reconstructing graph relationships from firms’ public websites and analyzing the patterns these relationships exhibit over time. He investigates whether firm-level knowledge transfer can be understood through website data, how a firm’s position in the value chain affects website structures, and whether firm production areas can be identified using NLP. His work uses Common Crawl data to address these broad questions.
Hernan holds a Master’s degree in Political Science from UCSD and is currently completing his PhD in Computer Science at the Vienna University of Technology. He has previously worked as a software developer at JPMorgan Chase and as an algorithm engineer at ZhiZhouKeji in Beijing. His interests lie in big data manipulation, visualization, and time series causality detection.