The Projects

The project analyzes biases in AI systems through two branches: computer vision and NLP. The computer vision branch examines biases in image recognition models using CNNs and explainable AI techniques like Grad-CAM and LIME. The NLP branch evaluates biases in LLMs using tools like word embeddings, the Large Language Model Bias Index, and BiasAsker, assessing biases and identifying stereotypes. The goal is to develop bias measurement tools and promote fairer AI systems.

The project models neural plasticity using Python and Brian2 to simulate spiking neural networks. It explores synaptic plasticity mechanisms like Hebbian learning and STDP, analyzing how neural connections evolve and adapt. Applications include Brain-Computer Interfaces (BCIs), neuroprosthetics, memory enhancement, and sensory restoration. Members also worked on case studies concerning adaptive prosthetic control and brain mapping and memory.

The project quantifies economic, educational, and policy determinants of digitalization in Europe using DESI. It applies statistical analysis and regression modeling to assess GDP per capita’s correlation with digital adoption, evaluates education metrics (e.g., STEM graduates, public expenditure) through multivariate regression, and analyzes policy frameworks in two EU countries to model their impact on digital infrastructure and digital skills development.