Anomaly Detection using GAN

The proposed research project targets anomaly detection of event data. The project has a duration of four months and aims to achieve two objectives: (1) to evaluate the effectiveness of a novel approach on GAN for real-world data, and (2) compare it to alternative methods. The intern will use existing research resources, and will apply them to real-world data provided by the partner, Acerta Analytics Solutions, Inc. to evaluate the different methods. The expected benefit to the partner organization, Acerta, is that the outcomes of the project will improve the existing a software platform to detect failures in automotive vehicles, and eventually to predict them before they happen.

Intern: 
Shailja Thakur
Faculty Supervisor: 
Mark Crowley
Province: 
Ontario
Partner University: 
Program: