Mr Sofonyas Tiruneh

Sofonyas Tiruneh is a Research Associate at the National Perinatal Epidemiology and Statistics Unit (NPESU), Centre for Big Data Research in Health (CBDRH), UNSW Sydney, Australia. He is a PhD candidate at Monash University (awaiting conferral), and his doctoral thesis has been accepted for the award of Doctor of Philosophy without further amendments or examination. His PhD project aims to develop and validate early pre-eclampsia risk prediction using readily available maternal factors, to facilitate early identification of high-risk women for timely intervention and better pregnancy outcomes.

Sofonyas has expertise in clinical epidemiology with extensive experience in clinical prognostic modelling, advanced biostatistical methods using big data, and evidence synthesis, including systematic reviews and meta-analyses. His research focuses on the development, external validation, and evaluation of clinical prediction models to improve maternal and perinatal health outcomes and facilitate clinical decision-making. He has extensive experience analysing electronic medical records (EMR), linked administrative data, observational studies, and randomised controlled trial data analysis. In addition, his methodological expertise encompasses both basic and advanced statistical methods, modified Poisson regression, and survival analysis. Sofonyas has contributed to over 50 peer-reviewed publications in reputable journals, including 14 as first author. He has demonstrated the ability to design and coordinate health research projects and has successfully led and collaborated on early-career-funded projects. His current and broader research interests include methodological research in clinical prediction modelling, comparing classical regression and machine-learning approaches, evidence synthesis, and facilitating insights into the implementation of clinical prediction models in practice by utilising readily available predictors to improve maternal health outcomes.

He has extensive experience analysing electronic medical records (EMR), linked administrative data, observational studies, and randomised controlled trial data analysis. In addition, his methodological expertise encompasses basic and advanced statistical methods, modified Poisson regression, and survival analysis.

Sofonyas contributed over 50 peer-reviewed publications in reputable journals, including 14 as first author. He has demonstrated the ability to design and coordinate health research projects and has successfully led and collaborated on early-career-funded projects.

His current and broader research interests include methodological research in clinical prediction modelling, comparing classical regression with machine-learning approaches, evidence synthesis, and facilitating insights into the implementation of clinical prediction models in practice by utilising readily available predictors to improve maternal health outcomes.