I am a PhD-track student in the International Doctorate in Economic Analysis - IDEA Program at the Universitat Autònoma de Barcelona and the Barcelona School of Economics, and a researcher at the Institute for Economic Analysis (IAE-CSIC). As part of the MOMENTUM Program (CSIC), I work on applying Artificial Intelligence to prediction and policy-relevant problems.

My research combines economics, econometrics, and machine learning to better understand social dynamics, anticipate crises, and inform decision-making. As part of the EconAI team, my recent work focuses on developing NLP applications and early-warning systems for international institutions such as the German Foreign Office, the United Nations, and the International Monetary Fund.

More broadly, I am interested in how data-driven methods can be used to improve forecasting, identify risks, and support policy design in complex environments.

Previously, I worked as a macroeconomic specialist at Peru’s Fiscal Council, where I focused on macroeconomic modeling, forecasting, and scenario analysis. This experience continues to shape my interest in applied, policy-oriented research.

I value transparent and reproducible research and enjoy building tools and pipelines that can be easily understood, replicated, and extended.

Recent Updates

• New working paper: Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events, with L. Mayoral, H. Mueller, C. Rauh, and M. Phillip. Working Paper.
• Released a pre-publication version of MacroPy, a toolbox for macroeconometric analysis in Python. Install via .whl here.
• New blog post: Bayesian VARs in Python. A step-by-step tutorial on Bayesian VARs, from scratch and with MacroPy.