Rate My Professor Kim-Anh Le Cao

Le Cao's research centers on computational statistics and biology, developing novel multivariate projection-based methods for integrating high-dimensional omics data including transcriptomics, proteomics, metabolomics, microbiome, and single-cell multi-omics. Her lab has created the mixOmics R package, featuring 19 methodologies for omics feature selection and data integration, ranking in the top 5% of Bioconductor downloads with around 80,000 annually. Key tools include DIABLO for multi-omics on the same samples, MINT for independent studies, mixMC for microbial communities, timeOmics for time-course data, LUPINE for microbial networks, and Φ-Space for single-cell phenotyping. She co-authored the book Multivariate Data Integration Using R: Methods and Applications with the mixOmics package (CRC Press, 2021). Highly cited publications encompass mixOmics: An R package for ‘omics feature selection and multiple data integration (Rohart et al., PLOS Computational Biology, 2017), DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays (Singh et al., Bioinformatics, 2019), and Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (Lê Cao et al., Biostatistics, 2011). Her awards include the Moran Medal from the Australian Academy of Science (2019), Georgina Sweet Award for Women in Quantitative Biomedical Science (2019), University of Melbourne Dean’s Award for Excellence in Research (mid-career, 2019), and Superstars of STEM (Science & Technology Australia, 2020–2022). Le Cao supervises PhD students on topics like microbiome analysis and single-cell multi-omics integration, teaches statistical genomics, and leads workshops on mixOmics.