Machine Learning

Poster presented at the IPP Summer School 2026 in Fregenal de la Sierra, Spain. It summarizes the project’s research questions, theoretical motivation, methodological approach, and planned analytical framework.

Book Chapter published in Democracy at the Crossroads – Challenges for Governance and Representation: Essays in Honour of Thomas Saalfeld for Governance and Representation published in Routledge

A web scraper written in R for extracting and processing content from the Boletín Oficial del Estado (BOE), the official gazette of Spain, as well as the historical BOE archives (“Gazeta”).

AI-based OCR and multilingual processing pipeline for historical electoral manifestos, including sentence-level extraction and translation

This study utilized PCA and K-means clustering to identify four distinct country clusters based on climate and energy consumption, revealing temperature as a key differentiator—particularly for cold, high-energy northern nations—while suggesting that finer clustering could shift focus toward socioeconomic factors like GDP.

This study developed machine learning models to predict employee attrition and monthly income, identifying job satisfaction and job level as key factors, respectively, while highlighting the potential of such techniques to inform corporate decision-making.

Conducted and communicated advanced data and economic analyses, leveraging causal inference and NLP techniques to support research efficiency and dissemination in academic and policy-making contexts

Text Mining and Natural Language Processing (NLP) course at Universidad de Snatiago Chile in Data Science course

Predicting Unemployment with Demographic and Labor Force Characteristics