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Professor Dianne Cook
Professor, Professor
Monash University
Di Cook is Professor of Business Analytics in the Department of Econometrics and Business Statistics at Monash. She is a Fellow of the American Statistical Association, and Ordinary Member of the R Foundation (elected). Her research is primarily in data visualisation, visualising high-dimensional spaces using tours with projection pursuit, and bridging the gap between statistical inference and exploratory data analysis. She has developed visualisations for virtual environments, used eye-trackers for assessing visual perception. The applications include bioinformatics, ecology and sport.
Research Interests:
data mining
Data Science
data visualisation
exploratory data analysis
multivariate methods
Statistical computing
Qualifications:
BS/Dip Ed (University of New England)
Msc (Rutgers University)
PhD (Rutgers University)
Publications
Invited talks, refereed proceedings and other conference outputs
Hirsch, M., Cook D., Lajbcygier P., & Hyndman R. J.
(2019). Revealing high-frequency trading provision of liquidity with visualization.
2nd International Conference on Software Engineering and Information Management. pp. 157-165.
Cook, D.
(2018). Myth busting and apophenia in data visualisation: is what you see really there?.
Ihaka Lecture, University of Auckland, NZ.
Cook, D.
(2018). To the Tidyverse and Beyond: Challenges for the Future in Data Science.
RStudio conference.
Tierney, N., McBain M., & Cook D.
(2018). Now you see it? Now you don’t? The role of graphics in identifying MCMC convergence.
ISCB ASC 2018.
Journal Articles
Lee, S., Cook D., & Lawrence M.
(2019). Plyranges: A grammar of genomic data transformation.
Genome Biology. 20(1), 4. doi: 10.1186/s13059-018-1597-8
Chowdhury, N. Roy, Cook D., Hofmann H., & Majumder M.
(2018). Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data.
Journal of Computational and Graphical Statistics. 27(1), 132 - 145. doi: 10.1080/10618600.2017.1356323
Cook, D., Laa U., & Valencia G.
(2018). Dynamical projections for the visualization of PDFSense data.
The European Physical Journal C. 78(9), doi: 10.1140/epjc/s10052-018-6205-2
Publicly available softwares
Wang, E., & Cook D.
(2020). tsibbletalk.
Tierney, N., Cook D., & Prvan T.
(2019). brolgar: BRowse Over Longitudinal data Graphically and Analytically in R.
Gupta, S., Hyndman R. J., Cook D., & Unwin A.
(2019). gravitas: Explore Probability Distributions for Bivariate Temporal Granularities.
Hyndman, R. J., Wang E., & Cook D.
(2019). feasts: Feature Extraction And Statistics for Time Series.
O'Hara-Wild, M., Hyndman R. J., Wang E., Cook D., Talagala T., & Chhay L.
(2019). feasts: Feature Extraction And Statistics for Time Series.
O'Hara-Wild, M., Hyndman R. J., Wang E., Cook D., & Athanasopoulos G.
(2019). fabletools: Core Tools for Packages in the 'fable' Framework.
Tierney, N., Cook D., McBain M., & Fay C.
(2018). naniar: Data Structures, Summaries, and Visualisations for Missing Data.
Wang, E., Cook D., Hyndman R. J., & O'Hara-Wild M.
(2018). tsibble: Tidy Temporal Data Frames and Tools.
Wang, E., Cook D., & Hyndman R. J.
(2018). sugrrants: Supporting Graphs for Analysing Time Series.
Wang, E., Cook D., & Hyndman R. J.
(2017). sugrrants: Provides ‘ggplot2’ graphics for analysing time series data..
Cook, D., Ebert A., Hofmann H., Hyndman R., Lumley T., Marwick B., et al.
(2017). eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia.
