My name is Riccardo Massidda, and I'm a second-year PhD student at the University of Pisa.
My research lies at the intersection of machine learning, neural network interpretability, and causality.
Feel free to reach me out at.
arXiv Preprint (Accepted Oral @CLeaR2023)
Causal abstraction provides a theory describing how several causal models can represent the same system at different levels of detail. In this work, we extend existing causal abstraction proposals to soft parametric interventions.
European Conference on Machine Learning (ECML 2022)
Definition of a framework to align ontological concepts and neural directions. Design of ablation experiments to validate the causal role of neurons within semantically coherent clusters.
2020 Evaluation of NLP and Speech Tools for Italian (EVALITA)
Definition of a hierarchical architecture based on sentence-embeddings (USE, LaBSE, SBERT) to solve a document dating task at different granularity levels.
2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Development of a data-driven bioinformatic model for the simulation of genomic variants and next-generation sequencing (NGS) reading errors.