About the event
Mathematical models of biological population growth are used to understand and predict rates coral re-growth after some disturbance, such as a cyclone or runoff event. Unlike physics or engineering where questions of model selection are largely settled, the question of model selection is a major barrier when dealing with applications in the life sciences where laws of biology are elusive. In this talk we discuss likelihood-based approaches for model selection using coral reef re-growth data, focusing on the profile-likelihood as a very simple tool to assess parameter identifiability, which we extend to model selection. Our results suggest that standard approaches used to model and predict coral re-growth can lead to misleading outcomes.
About the Presenter
I am a professor of Mathematics at Queensland University of Technology, Australia. Formerly, I held positions as an Australian Research Council Future Fellow (2014-2018) and as an Australian Research Council Postdoctoral Research Fellow (2006-2009). In 2012 I was awarded the JH Michell Medal for excellence in Research by ANZIAM (Australian and New Zealand Industrial and Applied Mathematics), which is a decision of the Australian Mathematical Society. In 2020 I was awarded the EO Tuck Medal for outstanding research and distinguished service by ANZIAM.
My work in mathematical biology began as a postdoc, where I changed fields after originally being trained in civil engineering. Early research interests included using continuum mathematical models to interpret in vivo experiments in developmental biology focusing on neural crest cell migration. Later projects involved developing stochastic mathematical models of these kinds of in vivo experiments and understanding how to construct approximate continuum limit descriptions of these stochastic models. More recently, I have been working at the interface of mathematical modelling, uncertainty quantification, parameter estimation and identifiability analysis. In general, my work involves deploying these mathematical tools to understand various biological and biophysical applications that include studying tissue growth on 3D-printed scaffolds, in vitro observations of cell migration and proliferation using fluorescent cell cycle labels, as well as tumour spheroid experiments.
|Location:||Zoom - online|
|Start Date:||20/06/2022 [add to calendar]|
|Start Time:||1:00 PM|
|End Time:||2:00 PM|