Dr Kim-Anh Lê Cao (University of Melbourne, Brisbane Australia) graduated from her PhD in 2008 at Université de Toulouse, France. She was awarded the triennial Marie-Jeanne Duhamel prize from the French Statistical Society for her phD thesis in Applied Statistics in 2009. Shortly after her graduation she relocated to Australia as a postdoctoral fellow in Prof Geoff McLachlan group at the Institute for Molecular Bioscience (IMB) at the University of Queensland (UQ). From 2009 to 2013 she was appointed as a Research Biostatistician at QFAB Bioinformatics (UQ) where she developed an extensive network of collaborations with academic, government, and industry sectors and a multidisciplinary approach to her research. In 2014 Dr Lê Cao was hired at the UQ Diamantina Institute (UQDI) and was awarded the highly prestigious Career Development Fellowship from the NHMRC (2015 - 2019). She established and led the UQDI Biostatistics Facility to provide statistical support to other UQ and UQDI researchers. In 2017, Dr Kim-Anh Lê Cao started her senior lecturer position in Statistical Genomics at the University of Melbourne. Dr Lê Cao's main research focus is on variable selection for biological data (`omics' data) coming from different functional levels by the means of multivariate dimension reduction approaches. Since 2009, her team has been working on developing the statistical R toolkit mixOmics that is dedicated to the integrative analysis of `omics' data, to help researchers make sense of biological big data. She and her team regularly run statistical training workshops and short series seminars and mixOmics multi-day workshops.
My aim is to build a strong multi-disciplinary computational and statistical team that will play a key role in biologically-relevant fields by working at the interface between biologists, bioinformaticians, statisticians and clinicians. As such, my research interests lie in multivariate statistical analysis, with a strong focus on the statistical analysis of high-throughput biological data, including longitudinal data, 'omics data integration and identification of biomarkers. I welcome students interested in developing innovative statistical methodologies to answer cutting-edge biological and medical research questions, applying those methods to areas informed by biology, as well as implementing those methods into open source softwares for the scientific community. Areas of application include, but are not limited to: microbiome, single-cell sequencing data, omics technologies, stemm cells and biomedical research.