Pipeline Unificado para Tomada de Decisão Financeira com Agentes Multimodais e Descentralizados

a clean, educational agent framework for learning and prototyping

VISION is a framework for robust and interpretable code vulnerability detection using counterfactual data augmentation. It leverages GNNs, LLM-generated counterfactuals, and graph-based explainability to mitigate spurious correlations and improve generalization on real-world vulnerabilities (CWE-20).

Análise Aprimorada de Intervenção para Ansiedade com LLM Fine-Tuned

My school project (MSc Applied AI): Brain tumour classification with pre-trained models and a custom CNN

Explainability and Interpretability: Understanding and Implementing AI Explainability

Transformer-based models for stereotype detection in text with explainability via LIME, SHAP, and attention visualizations.

Method to generate counterfactuals using dichotomous (Binary search) logic

explainability of models applied to obstetric medical imaging

🏎️ Predict Formula 1 podium finishers with 96.6% accuracy using machine learning and extensive telemetry data for insightful race forecasts.