Dr Dilki Jayasinghe: Science Meets Parliament - A first-hand perspective

Science Meets Parliament is Australia's premier science-policy engagement event, bringing together researchers, policymakers, and parliamentary representatives to strengthen the role of science in public policy. In this showcase, Dilki will provide an overview of the program and explain the opportunities available for UQ researchers to attend, what to expect Dr Dilki Jayasinghe, Biostatistics Research Fellow, Dermatology Research Centre, Frazer Institute
Dr Dilki Jayasinghe’s research spans melanoma epidemiology, medical imaging, and artificial intelligence (AI), with a focus on developing and applying quantitative methods to improve disease understanding, early detection, and risk prediction. She has experience working with large-scale longitudinal health datasets, medical imaging data, and the validation and application of AI-based tools in dermatology and melanoma research. Her work is grounded in interdisciplinary collaboration and the integration of statistical methods into clinical and translational research.at the event, and how to prepare beforehand to maximise the experience. She will share her experience engaging with a parliamentarian and participating in sessions on science communication and policy engagement. Dilki will also discuss the professional development opportunities, networks, and potential career outcomes that can arise from participating in this unique national initiative.

Bio

Dr Dilki Jayasinghe, Biostatistics Research Fellow, Dermatology Research Centre, Frazer Institute
Dr Dilki Jayasinghe’s research spans melanoma epidemiology, medical imaging, and artificial intelligence (AI), with a focus on developing and applying quantitative methods to improve disease understanding, early detection, and risk prediction. She has experience working with large-scale longitudinal health datasets, medical imaging data, and the validation and application of AI-based tools in dermatology and melanoma research. Her work is grounded in interdisciplinary collaboration and the integration of statistical methods into clinical and translational research.