Our research activities have been always motivated by real problems arising in Physics, Medicine and Biology. In particular, we have focused on the analysis of high-throughput data by means of both statistical and computational methods. Our experience includes Bayesian inference, Denoising, Dimension reduction techniques, Functional data analysis, Multiple hypothesis testing, Multivariate statistics, Non-parametric regression, Supervised and Unsupervised learning, Variable selection, and Wavelets. More recently, the interest has been deeply focused on the high-dimensional data problems arising in genomics. We have worked on a variety of problems in statistical genomics, including methods for analysis of microarray time course gene expression data, methods for the identification of transcription factor binding sites using variable selection approaches, and methods for analysis of Next Generation Sequencing Data. Current activities of BioinfoLab involve the development of statistical methods and tools for next generation sequencing with application to Epigenomics and Transcriptomics.

BioinfoLab members partecipate the Computational & Biology Open Laboratory (ComBOlab) multidisciplinary initiative.