Graph neural networks
Instructor: Fabrizio Silvestri
This course thoroughly introduces Graph Neural Networks (GNNs), tailored for Ph.D. students with a background in scientific areas. It offers a balanced view of both theoretical aspects and practical applications of GNNs. We will start with the basics of machine learning, graph theory, and neural networks to build a foundation for understanding GNNs. The course will cover essential GNN architectures, including Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), along with recent developments in the field. At the end of the course, we will also cover some aspects regarding the explainability of GNN models' predictions.
Program analysis: from proving correctness to proving incorrectness
This course offers a focused exploration of formal methods in software development, with some emphasis on the shift of perspectives after Peter O'Hearn's influential paper on incorrectness logic. Instead of exploiting over-approximations to prove program correctness like done with classical formal methods, incorrectness reasoning exploits under-approximations for exposing true bugs.
The overall goal of incorrectness methods is to develop principled techniques to assist programmers with timely feedback about the presence of true errors, with few or zero false alarms.
The course will overview different approaches, like program logics, pointer analysis, and abstract interpretation, for both over- and under-approximation, as well as their combination.
Large Language Models
Instructor(s): Danilo Croce
The evolution of Large Language Models (LLMs) has marked a profound transformation in computational linguistics and computer science. This course aims at introducing the main characteristics of LLMs.