Statistical applications in cancer research; Statistical applications in climate change; Computational biology; Spatio-temporal statistics; Machine learning and data mining.
Gen Nowak is a Senior Lecturer in Statistics in the Research School of Finance, Actuarial Studies and Statistics. Gen’s research focuses on developing and applying statistical methodologies to answer questions in a variety of fields including cancer research and climate change. He has worked on statistical applications in lung cancer and breast cancer and has developed various spatio-temporal models for modelling rainfall. Gen also has expertise in machine learning and data mining, having undertaken extensive research in clustering and regularised models in the context of high dimensional data. His research has appeared in leading scholarly journals including Environmental and Ecological Statistics, Computational Statistics and Data Analysis, Journal of Hydrology and Biostatistics.
G. Nowak and A. H. Welsh 2020, ‘Improved Prediction for a Spatio-Temporal Model’, Environmental and Ecological Statistics, doi:10.1007/s10651-020-00447-3.
G. Nowak, A. H. Welsh, T. J. O’Neill, L. Feng 2018, ‘Spatio-Temporal Modelling of Rainfall in the Murray-Darling Basin’, Journal of Hydrology, vol. 557, pp. 522-538, doi:10.1016/j.jhydrol.2017.11.021.
M. E. Abdel-Latif, G. Nowak, B. Bajuk, K. Glass and D. Harley 2018, ‘Variation in Hospital Mortality in an Australian Neonatal Intensive Care Unit Network’, Archives of Disease in Childhood Fetal and Neonatal Edition, vol. 103, pp. F331-F336, doi:10.1136/archdischild-2017-313222.
L. Feng, G. Nowak, T. J. O’Neill and A. H. Welsh 2014, ‘CUTOFF: A Spatio-Temporal Imputation Method’, Journal of Hydrology, vol. 519, pp. 3591-3605, doi:10.1016/j.jhydrol.2014.11.012.
S. Roberts and G. Nowak 2014, ‘Stabilizing the Lasso Against Cross-Validation Variability’, Computational Statistics and Data Analysis, vol. 70, pp. 198-211, doi:10.1016/j.csda.2013.09.008.
G. Nowak, T. Hastie, J. R. Pollack and R. Tibshirani 2011, ‘A Fused Lasso Latent Feature Model for Analyzing Multi-Sample aCGH Data’, Biostatistics, vol. 12, no. 4, pp. 776-791, doi:10.1093/biostatistics/kxr012.
G. Nowak and R. Tibshirani 2008, ‘Complementary Hierarchical Clustering’, Biostatistics, vol. 9, no. 3, pp. 467-483, doi:10.1093/biostatistics/kxm046.
Gen has taught a number of courses in statistics, including STAT7055 Introductory Statistics for Business and Finance, an important postgraduate statistics course in the College of Business and Economics. Gen’s commitment to teaching excellence continues to be recognised through the various awards and nomination he has received to date. These include the College of Business and Economics Award for Teaching Excellence in 2019 and nominations for the Vice-Chancellor’s Award for Teaching Excellence in 2018 and 2020. In addition to teaching courses in statistics, Gen also supervises Honours and PhD students with research projects which have included topics such as spatio-temporal data analysis and penalised regression.
STAT7055 Introductory Statistics for Business and Finance
STAT1008 Quantitative Research Methods
STAT1003 Statistical Techniques