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Hi Everyone! This month we are excited to host Sevvandi Kandanaarachchi, who presented her work on anomaly detection. Why are anomalies important? Because they tell us a different story from the norm. An anomaly or an event might signify a failing heart rate of a patient, a fraudulent credit card activity, or an early indication of a tsunami. As such, it is extremely important to detect anomalies or anomalous events. Anomalies are rare events. As a result, standard accuracy measures do not apply. But then, how do we evaluate an Anomaly Detection (AD) method? If we want to compare two or more AD methods, what kind of simple tests can we do? What are the data repositories that are available for AD? We will also discuss an ensemble method for AD. Constructing an AD ensemble is challenging because the class labels are not known. We will look at an unusual ally from psychometrics – Item Response Theory – to help us in this construction. Sevvandi is an applied mathematician at RMIT Mathematics working in data science and analytics. She uses statistics, mathematics and machine learning for her research. Her main research interest is anomaly and event detection. Examples of anomalies include finding alien life on a planet far, far away, or closer to home, a stock market crash. She also likes working on real world problems, especially ones that are motivated by industry.