I completed my PhD in Statistics at Monash University, Australia, under the supervision of Professor Rob J. Hyndman and Professor Kate Smith-Miles. My research focuses on statistical machine learning and data mining, and in particular the development of novel methods and tools for analyzing complex data. I am also strongly committed to develop open source software tools to facilitate reproducible research.
Water-quality sensors are exposed to changing environments and extreme weather conditions and thus are prone to errors, including failure. These technical errors make data unreliable and untrustworthy and affect performance of any subsequent data analysis. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a feature based procedure, named oddwater, for detecting technical outliers in water-quality data derived from in situ sensors.
Anomalies can be the main carriers of significant and often critical information, and the identification of anomalies is a key task in many fields such as cyber security, intrusion detection, water quality monitoring, system health monitoring, environmental monitoring. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a framework that provides early detection of anomalous series within a large collection of streaming time-series data.
Invited talks, refereed proceedings and other conference outputs