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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.
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.
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.
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.
