Kes Ward, University of Lancaster on 'Anomaly Detection at the Edge '

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Kes is a mathematics PhD student looking at anomaly detection in real-time data streams. 

He is based at the Statistics and Operational Research for Industry (STOR-i) centre for doctoral training at Lancaster University. 

His work is part-funded by BT to develop new methods for finding changes in network traffic flowing through sensors. This helps identify problems as soon as they happen, so they can be fixed early before causing more issues.

Anomaly Detection on the Edge

In an Internet of Things where everything is collecting and analysing its own data, we need edge analytics to help us sort the meaningful from the muck without breaking the computational bank. In this talk I will present a new statistical method for finding anomalies of different shapes and sizes in a real-time data signal, while working under extremely tight computational constraints.