Shalem LeemaqzView profile
Research Fellow
Dr. Shalem Leemaqz is a Postdoctoral Fellow and adjunct staff at the Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide. He maintains dual research appointments as a postdoctoral researcher at the Pregnancy Health and Beyond (PHaB) Lab at the College of Medicine and Public Health, Flinders University, and as a statistician at South Australian Health and Medical Research Institute. With a background spanning Mathematics/Statistics and Electronic Systems Engineering, Dr. Leemaqz brings expertise in computer architecture, machine learning techniques, and programming (particularly R statistical programming) to medical research. His primary research focus involves developing statistical models to predict pregnancy complications through advanced mathematical and data mining approaches for high-dimensional data analysis. During his PhD (2010-2015) at the University of Adelaide, he pioneered a novel tiered modeling approach that classifies low-prevalence outcomes into risk levels, which was applied to develop screening tools for preeclampsia, preterm birth, intrauterine growth restriction, and gestational diabetes. Dr. Leemaqz's recent scholarly output (2023-2025) demonstrates significant contributions across multiple domains including pregnancy complications, placental biology, and transgender health research. His work consistently applies sophisticated statistical methodologies to address complex medical questions, with a particular emphasis on developing flexible algorithms for low-prevalence disease prediction. His research aims to create screening tools that classify patients into three risk levels to enable targeted interventions improving maternal and child health outcomes. Dr. Leemaqz is deeply committed to integrating rigorous statistical methodology with clinical medical research. His collaborative approach bridges statistics, bioinformatics, and clinical medicine to develop integrated methods for analyzing and combining genomic data with clinical information, ultimately advancing precision medicine approaches in pregnancy care.



