Scopul acestui articol este de a analiza numarul de locuri disponibile (atat cu finantare de la bugetul de stat, cat si in regim de taxa) oferite de Facultatea de Drept din cadrul Universitatii Alexandrul Ioan Cuza din Iasi pentru cele 12 specializari ale programelor de master propuse

ATIVIDADE 3 - PROF. ANTÔNIO INÁCIO

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

Rare Disease Atlas (Notre Dame summer research)

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.