Rate My Professor Agus Salim

Associate Professor Agus Salim is an Associate Professor in Biostatistics at the Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, holding a joint appointment with the School of Mathematics and Statistics. He earned his PhD from the National University of Ireland and a Bachelor's Degree with Honours from Bogor Agricultural University. His previous academic affiliations include La Trobe University, National University of Singapore, and Australian National University. Salim leads the Analytical Methods for Big High-Dimensional Biomedical Data research group within the Centre for Epidemiology and Biostatistics. He is also an Affiliated Researcher with the Victorian Centre for Biostatistics.

Salim's research centers on biostatistics, with key interests in developing risk prediction models that integrate high-dimensional omics data and traditional risk factors, statistical methods for high-dimensional omics data analyses, and interactive analyses of continuous glucose monitoring data. He possesses extensive experience in both methodological and collaborative research, having secured multiple grants from the National Health and Medical Research Council and the Australian Research Council. In addition to his research contributions, he serves as an Associate Editor for BMC Medical Research Methodology and as a statistical reviewer for Diabetic Medicine. Notable publications include 'FunRich: An open access standalone functional enrichment and interaction network analysis tool' (Proteomics, 2015), 'Smoking as a risk factor for dementia and cognitive decline: a meta-analysis of prospective studies' (American Journal of Epidemiology, 2007), 'Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis' (Nature Medicine, 2018), 'Accessory subunits are integral for assembly and function of human mitochondrial complex I' (Nature, 2016), and 'Statistical challenges associated with detecting copy number variations with next-generation sequencing' (Bioinformatics, 2012). These works have significantly advanced biostatistical applications in biomedical fields such as epidemiology, oncology, and diabetes research.