Dr. Mel R Mylek is a research fellow at the Health Research Institute, University of Canberra. She was
awarded her PhD at the Australian National University in 2021, exploring the social acceptability of fuel management strategies used to reduce bushfire risk to life and property. Her research focuses on the health and wellbeing of different groups of Australians, wellbeing and resilience in relation to experience of challenging times such as disasters, and social dimensions of natural resource management in Australia. Her recent work has been focused on wellbeing surveys, wellbeing measurements and indicator production, using both
quantitative and qualitative social science methodologies. She works extensively on the annual Regional Wellbeing Survey (RWS) and the annual Carer Wellbeing Survey (CWS): both nationwide surveys examining the wellbeing of individuals, different groups of people, communities and carers.
Dr. Theo Niyonsenga is an Associate Professor of Biostatistics at the University of Canberra (UC) Faculty of
Health, and a member of both UC Health Research Institute (HRI) and Centre for Research and Action in Population Health (CeRAPH). He was trained in Mathematics, Physics & Engineering Sciences (Bachelor of Science, National University of Rwanda, 1979–1982); in Mathematical Statistics (Master of Science
and PhD, University of Montreal, Canada, 1982–1991); and completed his postdoctoral research training in Biometry (University of Montreal, Canada, 1991–1992). Dr. Niyonsenga commenced his continuing position with the University of Canberra by the end of January 2017. Prior to this relocation, he worked at the University of South Australia from September 2013 till January 2017. He has more than 20 years of experience working as a biostatistician in research, teaching and statistical consulting. He developed and applied statistical methods to collaborative research projects focusing on, but not limited to, the areas of multivariate data analysis methods such as structural equations modelling, longitudinal and multi-level data analysis and spatial statistics with application to spatial epidemiology.
