Title: Topology-based Explanations for Neural Networks

Abstract:
The seminar will be a summary of the research activities conducted on the topic of Explainable AI. The first part of the talk will be about the development of Self-Explainable Graph Neural Networks. Successively, the discussion will shift towards providing logic explanations for neural networks' predictions. Finally, different applications will be presented, spanning NLP, Drug Discovery and Reinforcement Learning.

BIO:

Alessio Ragno is a Ph.D. student in Artificial Intelligence at Sapienza University of Rome, supervised by Professor Roberto Capobianco. With a passion for applying AI to various scientific realms, Alessio focuses on Explainable AI (XAI) to enhance understanding and usability.
His research involves creating topology-based XAI methods tailored to specific AI models, aiming to make neural network predictions more interpretable. Prior to his Ph.D., Alessio earned Master's and Bachelor's degrees in AI & Robotics and Computer and Control Engineering, respectively.
Alessio gained practical experience in AI's application to chemistry and drug discovery during his time at the University of North Carolina and collaborations with Sapienza University of Rome's Pharmaceutical Chemistry and Technology Department. His dedication lies in making AI more accessible and transparent for meaningful scientific advancements.