CV

academic curriculum vitae.

Rasoul Norouzi, M.Sc., Ing.

Ph.D. Candidate in Social Science, Department of Methodology and Statistics, University of Tilburg, Netherlands

r.norouzinikjeh@tilburguniversity.edu
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Education

Ph.D. Candidate in Social Science

Expected completion: January 2027

Department of Methodology and Statistics, University of Tilburg, Netherlands

Supervisors: Dr. Caspar van Lissa, Dr. Bennett Kleinberg, Prof. Dr. Jeroen Vermunt.

Research focus: My PhD research centers on enhancing theory development in social science through the integration of systematic text mining reviews with advanced computational models, specifically Large Language Models (LLMs) and Graph Neural Networks (GNNs). This approach is designed to overcome traditional biases by extracting and analyzing causal relationships from textual data, thereby facilitating the generation of innovative behavioral hypotheses and contributing to theory advancement with comprehensive, data-driven insights.

M.Sc. by Research, Information Technology (IT)

2016-2019

Tarbiat Modares University of Tehran, Iran

Supervisors: Dr. Amir Albadvi, Dr. Elham Akhondzadeh.

Thesis: My master's thesis introduced an innovative method combining ontology and multilabel classifiers to mitigate cold start and matrix sparsity issues in recommender systems, achieving enhanced accuracy in predicting user interests over traditional collaborative filtering models.

Teaching Experience

Teaching Assistant, Computational Statistics with R

Oct 2023-present

Tilburg University

Sampling, Monte Carlo simulation, bootstrapping, permutation tests, clustering, PCA.

Bachelor Thesis Supervisor

Sep 2024-Feb 2025

TU Eindhoven, Computer Science

Two theses extending the PhD pipeline: clustering of causal concepts with HDBSCAN + BERTopic (8.5/10); domain-shift mitigation via fine-tuning (8/10).

Research Interests

  • Interdisciplinary research in AI and the social sciences
  • Text mining and machine learning applications in theoretical framework development
  • Data-driven insights for social science research

Technical Skills

Programming and Scripting

  • Python (Advanced): implementing machine learning packages, mathematical operations, loading and manipulating data, web scraping, and crawling web data.
  • JavaScript (Intermediate): writing functions, methods, and objects, including implementing TensorFlow.js in the browser.
  • R (Intermediate): statistical computation, including simulations and hypothesis testing.

Tools and Software

  • Data science and machine learning tools: proficient with NumPy, Pandas, SciPy, scikit-learn, TensorFlow, PyTorch, NLTK, Hugging Face, and Matplotlib for advanced data analysis and model development.
  • Web and DB technologies: basic knowledge of React, HTML, CSS3, and SQL; understanding of web application development, with limited experience in building progressive web applications powered by machine learning algorithms.
  • Other technical skills: version control with Git and GitHub, including cloning, forking, pushing, and pulling.

Publications

  1. Norouzi, R., Kleinberg, B., Van Lissa, C. J., & Vermunt, J. (2024, April 10). Capturing Causal Claims: A Fine-Tuned Text Mining Model for Extracting Causal Sentences from Social Science Papers. Link.
  2. Joireman, J., Van Lissa, C. J., Van Lange, P. A. M., Kleinberg, B., Norouzi, R., & Balliet, D. (2024, April). A text mining systematic review of the social dilemma literature. Psychological Bulletin.
  3. Norouzi, R., Baziyad, H., Akhondzadeh, E., & Albadvi, A. (2022). Developing tourism users profiles with data-driven explicit information. Link.
  4. Hosseini, S. M. R., Baziyad, H., Norouzi, R., et al. (2021). Mapping the intellectual structure of GIS-T field (2008-2019): A dynamic co-word analysis. Scientometrics. Link.
  5. Baziyad, H., Norouzi, R., & Albadvi, A. Mapping the Intellectual Structure of the Internet of Things (IoT) Field Based on Web Content: A Co-word Analysis. 4th International Congress of Electrical, Computer and Mechanical Engineering. Link.
  6. Baziyad, H., Shirazi, S., Hosseini, S. M. R., & Norouzi, R. (2019). Mapping the intellectual structure of epidemiology with use of co-word analysis. Journal of Biostatistics and Epidemiology. Link.