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). 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.
(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.
(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.
