My main interests lie in the field of computational political science and applied data analysis, particularly in the study of socio-political behavior.

I am currently collaborating on several projects:

  • The REPBIAS (“The sources of representation bias: An approach from political and economic history”, UOC/IPERG/UAB) research project, creating and evaluating models that allow predicting the consequences of manipulating electoral institutions in controlled environments.

    Using these models, we examine how electoral systems distort the functioning of representative democracy, with a particular focus on seat-allocation bias and how this phenomenon compares to other institutional mechanisms (such as district magnitude, electoral thresholds, party concentration, or turnout differences).

  • The DIGIPOL “The Digitalization of Politics: Political Parties and Social Networks. Spain in Comparative Perspective” project within the CNSC Research Group at IN3 (UOC).

    Scraping the data and analyzing the strategy and use of social media by political parties in Spain. This includes studying cross-platform behavior, identifying dominant actors, mapping the circulation of partisan messages, and examining intra-party and inter-party interactions. The project also evaluates the quality of political debate online, the extent to which party structures are reproduced on social networks, and whether these platforms enable new issues, leaders, dissent, and greater inclusion of women and young people.

  • The VEARLYDEM project “Voting in Early Democracies: The Case of the Spanish Second Republic”. My work focuses on digitizing and analyzing historical electoral data and political discourse leveraging new OCR techniques such as generative AI models to extract and structure information from historical documents.


I have also collaborated in the following projects:

  • Within former GEOCONDAH research group (UOC/ICIP) in the “Nuclear deterrence, education and transformational change” project, which examined contemporary political discourse surrounding the renewed relevance of nuclear deterrence in the context of the war in Ukraine, with the aim of understanding the role of higher education in promoting a discourse of peace.

    A related publication is available here: Rajmil, D., Morales, L., García Juanatey, A., & Carbonell, D. (2025). European Parliament Nuclear Politics: The Debate within the No-Debate. Journal for Peace and Nuclear Disarmament, 1–8. https://doi.org/10.1080/25751654.2025.2589686

  • AGEAI: “Ageism in AI: new forms of age discrimination and exclusion in the era of algorithms and artificial intelligence” project within the CNSC research group at the IN3 institute of the University and the WZB. The project aims to develop automated procedures for detecting and analyzing age biases in images generated by generative AI systems.



If you wonder about my background, I attended:

  • B.A. in Political and Administration Sciences (Comparative Politics track), Universitat de Barcelona (2019)
  • Postgraduate Expert Degree in Data Analysis for Political Analysis and Public Management (Research Methodology and Quantitative Methods), Universitat de Barcelona (2021)
  • MSc in Political Analysis and Governance in Universitat Oberta de Catalunya (2025)



Some of the skills I have developed along the way
  • Data Analysis & Visualization — performance benchmarking, trend identification, correlation analysis, and development of clear analytical visual outputs.
  • Data Mining & Retrieval — API integration, Open Data exploitation, scientific dataset handling, and advanced web scraping/crawling.
  • Statistical Methods — descriptive and inferential statistics, hypothesis testing, Q-tests, t-tests, and robust analytical validation.
  • Feature Engineering & Model Optimization — feature selection, dimensionality reduction, model interpretability, cross-validation strategies, and rigorous model evaluation.
  • Machine Learning (Supervised & Unsupervised), including:
    • Regression models (linear, polynomial, logistic)
    • Ensemble methods (Random Forest, XGBoost)
    • Clustering techniques (K-means)
    • Principal Component Analysis (PCA)
    • Classification algorithms
  • Neural Networks — design, development, training, and evaluation of deep learning models.
  • Natural Language Processing — TF-IDF, topic modelling (LDA), vector embeddings (Word2Vec), dimensionality reduction (t-SNE), and sentiment analysis.
  • Generative AI — implementation of LLM-based workflows (RAG), and generation of synthetic images and video.
  • Time-Series Forecasting — predictive modelling using Prophet and LSTM architectures.
  • Deployment & Applications — development of interactive dashboards and data-driven applications.
  • Big Data Ecosystems — experience with Apache Spark, SparkML, Hadoop, and SparkSQL.




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