Biostatistics and Bioinformatics Section Committee

The SSA Biostatistics and Bioinformatics Section aims to connect and support biostatisticians and bioinformaticians across Australia. In July 2017, Sabine Braat and Karen Lamb were appointed as co-Chairs of the SSA Biostatistics and Bioinformatics Section and formed a committee to help guide biostatistics and bioinformatics events across Australia. The Biostatistics and Bioinformatics section committee includes individuals from a range of backgrounds with diverse expertise:

Sabine is a biostatistician at the University of Melbourne and has 20 years experience working in the pharmaceutical industry and university settings. She is an expert in the design, analysis, and reporting of clinical trials. 

Karen is a biostatistician and senior research fellow in the Generation Victoria (GenV) team at the Murdoch Children’s Research Institute  in Melbourne. Karen’s research interests are varied but her primary research focus is on advancing the understanding of neighbourhood effects on health through the application and development of novel statistical methods. 

Nicole De La Mata is an early-career biostatistician working at the The University of Sydney School of Public Health. Nicole’s research uses data linkage and observational cohorts to outcomes in people with end-stage kidney disease and assess disease transmission in organ donors in NSW.

Alysha De Livera is an academic in the Centre for Epidemiology and Biostatistics at the University of Melbourne, with applied and methodological interests in biostatistics and bioinformatics.

Jaimi Greenslade is a researcher and biostatistician at the Emergency and Trauma Centre of the Royal Brisbane and Women’s Hospital. Her research interest is in the diagnosis of patients with chest pain and infection.

Murthy Mittinty is a senior research fellow in the School of Public Health at the University of Adelaide. His research interests are in application of statistical methods to problems concerning public health, design and analysis of observational data, imputation of missing data and causal inference.

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