Graph neural networks: Variations and applications

  • Alexander Gaunt | University of Cambridge

Many real-world tasks require understanding interactions between a set of entities. Examples include interacting atoms in chemical molecules, people in social networks and even syntactic interactions between tokens in program source code. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects. I will give an overview of the research directions inside Microsoft that have explored different architectures and applications for deep learning on graph structured data.

Speaker Bios

I have a Masters and PhD in experimental quantum physics from the University of Cambridge and am a Junior Research Fellow at Trinity College. During my PhD I developed a method for trapping and studying cold atomic clouds in holograms, culminating in publications in Science, Nature and Nature Physics. I was an early adopter of CUDA for scientific simulations, which lead to an internship and postdoc positions in the MIP group at MSRC.

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