Priyanga Dilini Talagala | ARC Centre of Excellence for Mathematical and Statistical Frontiers

Associate Investigator

University of Moratuwa

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.

Research Interests:

Anomaly Detection

Applied statistics

Computational statistics

R Programming

Statistical machine learning

Time Series Analysis

Qualifications:

Ph.D. in Statistics, Monash University, Australia

Projects

A Feature‐Based Procedure for Detecting Technical Outliers in Water‐Quality Data From In Situ Sensors

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.

Anomaly Detection in Streaming Nonstationary Temporal Data

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.

Publications

Invited talks, refereed proceedings and other conference outputs

2021

Talagala, P. Dilini (2021). Technical Anomalies in Water-Quality Data From In-Situ Sensors - What, Why, and How?. ARCLP Workshop.

Talagala, P. Dilini, & Talagala T. (2021). Attention Towards Distance Education Tools During COVID-19 Pandemic - Evidence from Google Trends. OWSD 6th General Assembly and International Conference.

Talagala, P. Dilini (2021). Anomaly Detection in Spatio-Temporal Tensor Streams. 2021 Joint Statistical Meetings - American Statistical Association.

Talagala, P. Dilini (2021). Tidy Time Series Anomaly Detection for Load Forecasting. 41st International Symposium on Forecasting.

Talagala, P. Dilini (2021). Plot a lot with ggplot2 to find plots. R-Ladies Meetup.

Talagala, P. Dilini, Hyndman R. J., Smith-Miles K., Kandanaarachchi S., & Munoz M. A. (2018). Anomaly Detection in Non-Stationary Streaming Temporal Data. 38th International Symposium on Forecasting.

Talagala, P. Dilini, Hyndman R. J., Smith-Miles K., Kandanaarachchi S., & Munoz M. A. (2018). Outlier Detection in Non-Stationary Data Streams. Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.

Journal Articles