KSODI — toward interaction telemetry for AI systems. A structured, non-normative observation model for interaction dynamics (states, coherence, resonance) in human–AI and multi-agent settings. Light: AI literacy & reflection. Standard-Eval/Full: explainable drift observation for governance research. Status: active research, validation ongoing.

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).

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

Benchmarking a novel bio-informed RNN architecture under Dr. Cheng Zhang at KCL

a clean, educational agent framework for learning and prototyping

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

Análise Aprimorada de Intervenção para Ansiedade com LLM Fine-Tuned

Reproducible NLP research repo for extremist vs non-extremist social-media text classification.

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

Explainability and Interpretability: Understanding and Implementing AI Explainability

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

Method to generate counterfactuals using dichotomous (Binary search) logic

explainability of models applied to obstetric medical imaging

Exploring privacy-explainability trade-offs using differential privacy, federated learning, and membership inference attacks

An implementation of Text2Concept paper

The independent reimplementation; the fusion experiment (feature-level vs decision-level) and its negative result; the second-dataset (PaySim) generalization test; the systematic SHAP/LIME interpretability analysis; the cost/threshold framing; the serving system, explainability API, and demo; and the engineering conclusions drawn from all of it.