A principled interpretability framework based on token-level perplexity to test whether LLMs rely on linguistically relevant cues in benchmark evaluations.
Integrative multi-omics and explainable ML analysis for pathway-level signatures discovery in Multiple Sclerosis. Under review at Scientific Reports.
A Machine Learning pipeline comparing explainable AI and traditional statistical approaches for Multiple Sclerosis biomarker discovery.
Presented "ML Pipeline for Multiple Sclerosis Biomarker Discovery" at the CIBB25 conference.
Presented "ML Pipeline for Multiple Sclerosis Biomarker Discovery" at the BBCC24 conference.
Selected among the best Italian STEM students for a fully funded 2-week CS summer school by Fondazione ELICSIR, covering ML theory and biomedical applications.
Selected for a fully funded 3-day CS summer school on frontiers of Computer Science, organised by Fondazione ELICSIR and sponsored by Schmidt Sciences.