Introduction to Graph Neural Networks
In this talk, you will learn the theory behind GNNs, and look closely at the types of problems for which GNNs are well suited.
Graph Neural Networks (GNNs) are AI models designed to derive insights from unstructured data described by graphs. For different segments and industries, GNNs find suitable applications such as molecular analysis, drug discovery, prediction of developments in stock market, thermodynamics analysis, and even modelling of human brain. Unlike conventional CNNs, GNNs address the challenge of working with data in irregular domains. In this talk, I will provide an introductory overview of the theory behind GNNs, take a closer look at the types of problems that GNNs are well suited for, and discuss several approaches to model unstructured problems as classification or regression at various levels.
Ekaterina Sirazitdinova
Ekaterina is a senior data scientist at NVIDIA specialized in solving computer vision and video analytics problems by means of AI. Her current focus also includes deep learning inference optimization on embedded devices. Previously, Ekaterina was a research engineer applying deep learning to medical image analysis. She has also authored several peer-reviewed journal and conference publications on various applications of image-based 3D reconstruction, localization and tracking. Ekaterina received her Ph.D. in Computer Science and M.Sc in Media Informatics, she also holds a Diploma in Business Informatics.
