Dr Pablo A. Astudillo-Estévez

Pablo Astudillo-Estévez is a computational social scientist and Senior Lecturer in Data Science at SOAS University of London. 

He is also an Associate Fellow at the Institute for New Economic Thinking at the University of Oxford and External Faculty at the Complexity Science Hub in Vienna. He holds a PhD in Economic Geography and Complexity Economics from the University of Oxford and an MSc in Environment and Development from the University of Edinburgh.

His research sits at the intersection of data science, complexity economics and network science, with a focus on how economies and places evolve, learn and innovate. By analysing large-scale granular datasets, Pablo studies the structure and dynamics of firm-to-firm production networks — including their role in the propagation of economic shocks and systemic risk — to understand how regional economies transform and develop new productive capabilities. His recent work applies machine learning and network reconstruction methods to map supply chains at national and global scales, and he is currently investigating the structure and dynamics of informal and illicit economies through the use of large granular datasets. His work has been published in Nature Communications, the Journal of Economic Dynamics and Control, and Scientific Reports, among others.

Pablo has worked as a Data Scientist at the World Bank across the Finance, Competitiveness and Innovation Global Practice and the Poverty and Equity Global Practice, as well as at the International Finance Corporation. He has also served as a consultant to the Inter-American Development Bank and advises governments and the private sector on strategies for sustainable, technology-driven economic development and on harnessing data to address development challenges.

Previously, Pablo was a Research Fellow at the Growth Lab at Harvard University and a Visiting Fellow at MIT's Media Lab. He is an alumnus of the Santa Fe Institute's Global Complexity School.

• Computational Social Science
• Complexity Economics
• Network Science
• Production Networks and Supply Chains
• Machine Learning and Artificial Intelligence for Economics
• Economic Geography
• Informal and Illicit Economies
• Data Science for Development