Description
Machine learning has revolutionized the analysis of large-scale data samples in high energy physics and greatly increased the discovery potential for new fundamental laws of nature. Specifically, graph neural networks (GNNs), thanks to their high flexibility and expressiveness, have demonstrated superior performance over classical deep learning approaches in tackling data analysis challenges in particle physics. In this talk, I will go through the fundamentals of GNNs, the design of physics-driven GNN architectures, and their applications in solving data analysis challenges in ongoing and planned collider experiments. Prospects and possible future directions will also be discussed.
