Dr. Don Vicendese is a research fellow at the Centre for Epidemiology and Biostatistics at the University of Melbourne, an Adjunct Research Fellow with the School of Engineering and Mathematical Sciences at La Trobe University, and is an accredited statistician with the Statistical Society of Australia. He has received AUD 11.3 million in funding from NHMRC and Asthma Australia Grants.
Through working with a diverse range of inter/national research teams over the last 13 years, Dr. Vicendese creatively applied multiple statistical methodologies to span several health contexts including COPD, asthma, allergy, nutrition and cancer. His methodological contributions to 106 peer-reviewed publications include the following: machine learning methods to develop risk models to predict COPD and thunderstorm asthma; quantile regression to model hospital performance and treatment outcomes in colorectal cancer; and topological data analysis to model clusters of exposure variation to pollution-related lung health outcomes. With a strong track record in clinical prediction modelling, his work has directly informed patient risk stratification and clinical decision-making.
He currently leads an international collaboration with the Australian National University and the University of Tokyo to develop advanced algorithms for extracting morphological features from lung CT scans using topological data analysis.
Dr. Don Vicendese is a research fellow at the Centre for Epidemiology and Biostatistics at the University of Melbourne, an Adjunct Research Fellow with the School of Engineering and Mathematical Sciences at La Trobe University, and is an accredited statistician with the Statistical Society of Australia. He has received AUD 11.3 million in funding from NHMRC and Asthma Australia Grants.
Through working with a diverse range of inter/national research teams over the last 13 years, Dr. Vicendese creatively applied multiple statistical methodologies to span several health contexts including COPD, asthma, allergy, nutrition and cancer. His methodological contributions to 106 peer-reviewed publications include the following: machine learning methods to develop risk models to predict COPD and thunderstorm asthma; quantile regression to model hospital performance and treatment outcomes in colorectal cancer; and topological data analysis to model clusters of exposure variation to pollution-related lung health outcomes. With a strong track record in clinical prediction modelling, his work has directly informed patient risk stratification and clinical decision-making.
He currently leads an international collaboration with the Australian National University and the University of Tokyo to develop advanced algorithms for extracting morphological features from lung CT scans using topological data analysis.