My research mainly focuses on developing statistical methods for studying the influence of somatic DNA mutations on clonal cell populations. The ability to study gene expression profiles at single-cell resolution will be valuable for improving understanding of clonal dynamics and intra-tumoral heterogeneity in many types of cancer.
I am generally interested in constructing statistical models (mainly Bayesian-based and latent process models) for the analysis of biological data, deriving analytical model inference and implementing novel methodologies in open-source software.
2021 Best ECR Oral Presentation Award, Oz Single Cell (Sydney); Best ECR Oral Presentation Award, Victorian cancer bioinformatics symposium; Runner-up ECR Poster Award, Oz Single Cell (Melbourne)
2022 Best Fast-Forwarding Talk Award, AMSI BioInfoSummer
P. Qiao, C. Mølck, D. Ferrari, and F. Hollande. A spatio-temporal model and inference tools for longitudinal count data on multicolor cell growth. The International Journal of Biostatistics, 2018.
G. Qian, Y. Wu, D. Ferrari, Q. Puxue and F. Hollande. Semi-supervised clustering by iterative partition and regression with neuroscience applications. Computational Intelligence and Neuroscience, 2016.
ORCID profile: https://orcid.org/0000-0002-1521-9086