Dr Dan Pagendam

Dan leads the Hybrid Modelling team within the Statistical Machine Learning (StatML) group of the Analytics and Decision Sciences Program within CSIRO Data61. He has three degrees from the University of Queensland: a PhD in mathematics and statistics, an MSc in statistics and an undergraduate degree in environmental science. Dan joined CSIRO in 2010 as Postdoctoral Research Fellow and is passionate about public good research, particularly with regard to how probabilistic modelling can be used to help protect and manage our valuable natural resources.

Dan's research draws on the use of complex models and datasets to answer important scientific questions in a diverse range of fields including: population biology, hydrology and agriculture. In recent years, Dan's research has focused on the use of statistics and machine learning for biological control of invasive mosquito populations, prediction of groundwater dynamics and quantifying uncertainty surrounding predictions of soil carbon sequestration in agriculture.

Dan currently supervises PhD students and early research career fellows working on topics in statistics and machine learning. He was previously an associate editor for the Journal of Agricultural, Biological and Environmental Statistics (JABES). Dan has been a recipient of the CSIRO Health and Biosecurity Innovation and Science Excellence Award, (2021), CSIRO Chairman's Medal (2018), the CSIRO Medal for Impact from Science (2016).