Dr Priyanga Dilini Talagala
Associate Investigator
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
Publications
Invited talks, refereed proceedings and other conference outputs
(2021). Anomaly Detection in Spatio-Temporal Tensor Streams.
2021 Joint Statistical Meetings - American Statistical Association.
(2021). Tidy Time Series Anomaly Detection for Load Forecasting.
41st International Symposium on Forecasting.
(2021). Plot a lot with ggplot2 to find plots.
R-Ladies Meetup.
(2021). Technical Anomalies in Water-Quality Data From In-Situ Sensors - What, Why, and How?.
ARCLP Workshop.
(2021). Attention Towards Distance Education Tools During COVID-19 Pandemic - Evidence from Google Trends.
OWSD 6th General Assembly and International Conference.
(2020). Anomaly Detection in Streaming Time Series Data.
40th International Symposium on Forecasting.
(2020). Tensor-based anomaly detection in multivariate spatio-temporal data.
40th International Symposium on Forecasting.
(2019). Anomaly Detection in R.
useR! 2019.
(2018). Outlier Detection in Non-Stationary Data Streams.
Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.
(2018). Outlier Detection in Non-Stationary Data Streams.
2018 Joint Statistical Meetings (JSM2018).
(2018). oddstream and stray: Anomaly Detection in Streaming Temporal Data with R.
useR! 2018.
(2018). Anomaly Detection in Non-Stationary Streaming Temporal Data.
38th International Symposium on Forecasting.
Journal Articles
(2019). Anomaly Detection in High Dimensional Data.
arXiv. arXiv:1908.04000v1.
Publicly available softwares
Technical reports and unrefereed outputs
(2018). Anomaly Detection in Streaming Nonstationary Temporal Data.
