Webinar "Introduction to Graph Neural Networks"

On the webinar, we will talk about GNNs and the types of problems that GNNs are well-suited for. We will also discuss several approaches for modeling unstructured problems as classification or regression at various levels and more.

Dmitry Spodarets
Dmitry Spodarets

Data Phoenix team invites you all to our upcoming "The A-Z of Data" webinar that’s going to take place on April 6 at 16.00 CET.

  • Topic: "Introduction to Graph Neural Networks"
  • Speaker: Ekaterina Sirazitdinova, Senior Data Scientist at NVIDIA
  • Language: English
  • Participation: free (but you’ll be required to register)

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.