Stuart Lee
PhD Student
Monash University
Stuart is PhD candidate in statistics 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
Prizes, awards and special recognition
2019
ACEMS Business Analytics Prize 2019 was awarded to Stuart Lee. Awarded from the Monash University Department of Econometrics and Business Statistics.
$3000 awarded, in recognition of the high impact of Stuart Lee's research in an area where data analysis can often by tricky due to the size and complexity of the data sets
Funded by Rob Hyndman from ACEMS funding.
Publications
Invited talks, refereed proceedings and other conference outputs
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.
Cook, D.
(2019). Visualization of Data.
useR! 2019.
Cook, D., & Hofmann H.
(2019). Visualization of Big Biomedical Data.
SISBID '19.
Journal Articles
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. 1 - 13. doi: 10.1080/10618600.2019.1695624
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.v62.2,10.1111/anzs.12292
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
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. 1 - 13. doi: 10.1080/10618600.2019.1695624
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
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.
