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Know Your Neighbours: Machine Learning on Graphs

YOW! Data 2018

Machine learning has become ubiquitous in many applications. There are many accessible tools available to apply standard machine learning models to make predictions on data.

Typically, machine learning problems aim to predict something about an entity using some data about each entity. However, in the real world, entities - people, places or things - are connected to each other and can have complex interactions with their neighbours. We can use this information to improve our predictions, and to gain more insight into the network structure and how entities can affect each other.

This talk will introduce machine learning on graphs, give some examples where graph approaches give large improvements over standard machine learning techniques, and will demonstrate some tools that make graph machine learning more approachable.

Andrew Docherty

Sr. Research Engineer

CSIRO Data61

Australia

I am currently a research engineer at CSIRO's Data61, working to build a large-scale graph-based data analytics platform.  My research interests are classification and regression models on graphs, generative graph models, and time-sequence modelling.
 
Previously, I worked in medical image processing for retinal imaging in Canon's R&D center in Sydney. Prior to this I worked in numerical simulation of photonics devices at the University of Maryland, Baltimore County and at the University of Sydney.