Priyanga Dilini Talagala
PhD, Monash University, Australia




 



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Priyanga Dilini Talagala

PhD in Statistics

Monash University, Australia

I am a Senior Lecturer in the Department of Computational Mathematics, University of Moratuwa. I earned my PhD in Statistics from Monash University, Australia, under the supervision of Professor Rob J. Hyndman and Professor Kate Smith-Miles. I am a fellow of OWSD, a programme unit of UNESCO (2021-2022).

I am also an Associate Investigator of the Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Australia. Further, I am a co-founder of R-Ladies Colombo, a local chapter of the R-Ladies Global Organization.

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. You can find some of my projects here. Visit me on twitter, I post mostly about R, Statistics, Data Science and science in nature.

Thiyanga Talagala, PhD in Statistics, Monash University, Australia is my sister.

Interests

  • Computational statistics
  • Statistical machine learning
  • Machine learning interpretability
  • Anomaly detection
  • Time series analysis and forecasting
  • High dimensional data visualization
  • Streaming data mining
  • Responsible AI
  • Applied statistics
  • R programming

Education

  • PhD in Statistics, 2019

    Monash University, Australia

  • BSc (Hons) Special Degree in Statistics, 2013

    University of Sri Jayewardenepura, Sri Lanka

  • Batch first and Professor R A Dayananda Gold Medalist, 2013

Featured Publications




Featured Publications

Anomaly detection in high-dimensional data - in JCGS

This article proposes a framework to detect anomalies in high dimensional data using feature engineering.

Anomaly Detection in Streaming Nonstationary Temporal Data - in JCGS

This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary streaming time series data.






Recent Publications

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Responsible AI: The talk of the Town

This article discusses the significance of responsible AI for the protection of humanity in an exceedingly unpredictable future.

Anomaly Detection in High Dimensional Data

The algorithm, stray, which is specially designed for high-dimensional data, addresses the limitations of the state-of-art-method, the …

Anomaly Detection in Streaming Nonstationary Temporal Data

This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary …











Recent & Upcoming Talks

Plot a lot with ggplot2 to find plots

R Ladies Meetup (Virtual Event)

Anomaly Detection in Streaming Time Series Data

StatScale Seminar, Lancaster University, UK

Tensor-based anomaly detection in multivariate spatio-temporal data

40th International Symposium on Forecasting, Rio de Janeiro, Brazil

Anomaly Detection in R

useR! 2019, Toulouse , France.

A feature-based framework for detecting technical outliers in water-quality data from in situ sensors

39th International Symposium on Forecasting, Thessaloniki, Greece











Projects

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CRAN Task View: Anomaly Detection with R

CRAN Task View: Anomaly Detection with R.

oddstream - R package

oddstream {Outlier Detection in Data STREAMs}

oddwater - R package

oddwater{Outlier Detection in Data from WATER-quality sensors}

R-Ladies Colombo

R-Ladies is a worldwide organization whose mission is to promote diversity in the R community. R-Ladies Colombo chapter is a part of …

staplr - R package

A package containing a toolkit for PDF files

stray - R package

stray {Search and TRace AnomalY}

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