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Stuart Lee
PhD Student
Monash University
Stuart is postdoctoral research fellow at Monash Econometrics and Business Statistics.
His primary research interests are in the area of the exploratory data analysis with applications to bioinformatics and high dimensional data problems.
Research Interests:
Bioinformatics
data visualisation
exploratory data analysis
High-dimensional data analysis
Open Source Software Development
Publications
Invited talks, refereed proceedings and other conference outputs
Cook, D.
(2021). Human vs computer: when visualising data, who wins?.
Irish Statistical Association Gosset Lecture.
Cook, D.
(2020). Going beyond 2D and 3D to visualise higher dimensions, for ordination, clustering and other models.
International Statistical Ecology Conference 2020.
Cook, D., Tierney N., & Prvan T.
(2020). The paradox of the positive: exploratory tools for visualising the individuals in (multivariate) longitudinal data.
International Biometric Conference 2020.
Cook, D.
(2020). Making inference using data plots, with application to ecological statistics.
International Statistical Ecology Conference 2020.
Cook, D.
(2019). Visualization of Data.
useR! 2019.
Cook, D., & Hofmann H.
(2019). Visualization of Big Biomedical Data.
SISBID '19.
Cook, D.
(2019). Human vs computer: when visualising data, who wins?.
Data Science, Statistics and Visualisation 2019 (at the 62nd World Statistics Conference).
Cook, D.
(2019). Give Your Statistician Colleague Iris Bulbs for Their House Warming!.
Joint Statistics Meeting 2019.
Journal Articles
Laa, U., Cook D., & Lee S.
(In Press). Burning Sage: Reversing the Curse of Dimensionality in the Visualization of High-Dimensional Data.
Journal of Computational and Graphical Statistics. doi: 10.1080/10618600.2021.1963264
Gupta, S., Hyndman R. J., Cook D., & Unwin A.
(In Press). Visualizing Probability Distributions Across Bivariate Cyclic Temporal Granularities.
Journal of Computational and Graphical Statistics. 1 - 12. doi: 10.1080/10618600.2021.1938588
Morota, G., Cheng H., Cook D., & Tanaka E.
(2021). ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1Abstract.
Journal of Animal Science. 99(2), skaa402. doi: 10.1093/jas/skaa402
Polak, J., & Cook D.
(2021). A Study on Student Performance, Engagement, and Experience With Kaggle InClass data Challenges.
Journal of Statistics and Data Science Education. 29(1), 63 - 70. doi: 10.1080/10691898.2021.1892554
da Silva, N., Cook D., & Lee E-K.
(2021). A Projection Pursuit Forest Algorithm for Supervised Classification.
Journal of Computational and Graphical Statistics. 30(4), 1168-1180. doi: 10.1080/10618600.2020.1870480
Cook, D., Reid N., & Tanaka E.
(2021). The Foundation is Available for Thinking about Data Visualization Inferentially.
Harvard Data Science Review. Summer 2021(3.3), doi: 10.1162/99608f92.8453435d
VanderPlas, S., Röttger C., Cook D., & Hofmann H.
(2021). Statistical significance calculations for scenarios in visual inference.
Stat. 10(1), e337. doi: 10.1002/sta4.337
Su, S., Gouil Q., Blewitt M. E., Cook D., Hickey P. F., Ritchie M. E., et al.
(2021). NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data.
PLOS Computational Biology. 17(10), e1009524. doi: 10.1371/journal.pcbi.1009524
Wang, E., & Cook D.
(2021). Conversations in Time: Interactive Visualization to Explore Structured Temporal Data.
The R Journal. 13(1), 516-524. doi: 10.32614/RJ-2021-050
Forbes, J., Cook D., & Hyndman R. J.
(2020). Spatial modelling of the two‐party preferred vote in Australian federal elections: 2001–2016.
Australian & New Zealand Journal of Statistics. 62(2), 168 - 185. doi: 10.1111/anzs.12292
Wang, E., Cook D., & Hyndman R. J.
(2020). A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data.
Journal of Computational and Graphical Statistics. 29(3), 466-478. doi: 10.1080/10618600.2019.1695624
Laa, U., & Cook D.
(2020). Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics.
Computational Statistics. 35(3), 1171 - 1205. doi: 10.1007/s00180-020-00954-8
Laa, U., Cook D., & Valencia G.
(2020). A Slice Tour for Finding Hollowness in High-Dimensional Data.
Journal of Computational and Graphical Statistics. 29(3), 681 - 687. doi: 10.1080/10618600.2020.1777140
Lauter, A. N. Moran, Rutter L., Cook D., O’Rourke J. A., & Graham M. A.
(2020). Examining Short-Term Responses to a Long-Term Problem: RNA-Seq Analyses of Iron Deficiency Chlorosis Tolerant Soybean.
International Journal of Molecular Sciences. 21(10), 3591. doi: 10.3390/ijms21103591
VanderPlas, S., Cook D., & Hofmann H.
(2020). Testing Statistical Charts: What Makes a Good Graph?.
Annual Review of Statistics and Its Application. 7(1), 61 - 88. doi: 10.1146/annurev-statistics-031219-041252
Tierney, N J., & Cook D.
(2020). Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations.
arXiv. arXiv:1809.02264v2,
Wang, E., Cook D., & Hyndman R. J.
(2020). Calendar-Based Graphics for Visualizing People’s Daily Schedules.
Journal of Computational and Graphical Statistics. 29(3), 490 - 502. doi: 10.1080/10618600.2020.1715226
Kipp, M., Laa U., & Cook D.
(2019). Connecting R with D3 for dynamic graphics, to explore multivariate data with tours.
The R Journal. 11(1), 245-249. doi: 10.32614/RJ-2019-002
Rutter, L., VanderPlas S., Cook D., & Graham M. A.
(2019). ggenealogy: An R Package for Visualizing Genealogical Data.
Journal of Statistical Software. 89(13), 1-31. doi: 10.18637/jss.v089.i13
Rutter, L., Carrillo-Tripp J., Bonning B. C., Cook D., Toth A. L., & Dolezal A. G.
(2019). Transcriptomic responses to diet quality and viral infection in Apis mellifera.
BMC Genomics. 20(1), 412. doi: 10.1186/s12864-019-5767-1
Hofmann, H., Wickham H., & Cook D.
(2019). The 2013 Data Expo of the American Statistical Association.
Computational Statistics. 34(4), 1443 - 1447. doi: 10.1007/s00180-019-00923-w
Rutter, L., Lauter A. N. Moran, Graham M. A., & Cook D.
(2019). Visualization methods for differential expression analysis.
BMC Bioinformatics. 20(1), 458. doi: 10.1186/s12859-019-2968-1
Publicly available softwares
Hyndman, R. J., Wang E., O'Hara-Wild M., Cook D., & Caceres G.
(2019). fable: Forecasting Models for Tidy Time Series.
Cook, D., Ebert A., Forbes J., Hofmann H., Hyndman R. J., Lumley T., et al.
(2019). eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia.
