This dual methodological framework — combining machine learning for predictive feature selection with multilevel modeling for statistical inference — provides a comprehensive lens on vaccine hesitancy across Sub-Saharan Africa. The findings highlight both individual-level drivers (linguistic and civic integration, trust networks, lived service experience) and structural country-level factors, offering empirically grounded targets for context-aware public health interventions.

A model for the square root of BMI on the Belgian Health Interview Survey (BHIS) data set `bmi_voeg`, using as covariates the General Health Questionnaire score (GHQ), GP contact (SGP), and the demographic and socio-economic variables age, sex, smoking, education, and income. The modelisation takes into account the BHIS's design into account

Big-Data ML project — Hate Speech & Topic Structure of r/MensRights (KU Leuven, "Collecting & Analyzing Big Data", Jan 2026, group of 4) Graduate-level supervised ML + NLP pipeline in R (quanteda, quanteda.textmodels, topicmodels, mallet, LDAvis, caret, glmnet, pROC). Full workflow: EDA & preprocessing, tokenization, collocations, word frequencies; manual annotation with inter-coder reliability; trained & compared Naive Bayes, Logistic Regression, Random Forest with hyperparameter tuning & cross-validation; evaluation via confusion matrix, precision/recall, F1, ROC/AUC; error analysis of false positives/negatives; applied best model to detect hate speech across the corpus. Topic modelling: LDA with coherence & exclusivity selection, topic interpretation, and topic frequency over time; TF-IDF. Includes a transparent "GenAI use" statement. Relevance: rigorous, reproducible supervised-ML + text-mining at master's level, incl. the annotation/reliability step that empirical text studies require.

An in-depth research project in which I applied advanced techniques in mixed-effects modelling and imputation. This project was carried out as part of the multilevel modelling course within the QASS programme at KU Leuven.

Scraping data on TikTok that includes the hashtag #feminism is important for sociologists for several reasons. First, it allows us to understand the perspectives and experiences of individuals who engage with feminism on this popular social media platform. Second, analyzing the data can provide insights into how feminist ideas and activism are communicated and shared in a visually-driven and interactive format. Finally, examining the hashtag #feminism on TikTok can contribute to a broader understanding of the role of digital media in shaping feminist discourse and mobilization among younger generations.

In this report we made some basic visualization using the different variables in the world bank dataset which explores the relationship between people's socio-economics status and the life expectancy.

In this project, we analysed and predicted the sentiment of covid-19 related tweets based on the words they contain. The prediction models used in this project are Naive Bayes and Random Forest classification models. In addition, we proposed an exploratory analysis of the tweet data for a better understanding of the project. The project is an assignment in the Computational Methods for Text Analysis course at the Higher School of Economics SPB.