Research Interests

My research activity has been mostly motivated by real problems arising in the analysis of high-throughput data by means of statistical and computational methods.
Past experience includes Wavalets and Denoising, Non-parametric regression, Functional data analysis, Multiple hypothesis testing and Clustering. To handle such problem I have considered either frequentist and Bayesian approaches.
More recently, my interest has been focused on the analysis of high-dimensional data problems arising from genomics. I 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 research activity includes applications to Epigenomics and Transcriptomics, High dimensional data analysis and software development.
Since January 2012 I am the leading the Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab).
I am also a member of the Computational & Biology Open Laboratory (ComBOlab) and I participate to the Open Bioinformatics Laboratory (OBiLab) .

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Ongoing Research Projects.

INCIPIT (2015-2020)

HORIZON 2020/Marie Sklodowska Curie Action: INnovative Life sCIence Phd Programme in South Italy — INCIPIT

Epigen (2012-2018)

Progetto Bandiera Epigenomica:
Bioinformatics platform for integration of epigenomics data and systems biology.

InterOmics (2012-2018)

Progetto Bandiera InterOmics:
Development of an integrated platform for the application of "omic" sciences to biomarker definition and theranostic, predictive and diagnostic profiles.

Past Research Projects.

BioforIU (2012-2015)

PONa3_00025: BIOforIU Infrastruttura multidisciplinare per lo studio e la valorizzazione della Biodiversita marina e terrestre nella prospettiva della Innovation Union.

Diagen-sc (2012-2015)

PON01_02460: Studio per la realizzazione di un presidiodiagnostico per l'individuazione distrategie terapeutiche personalizzate peril diabete di tipo 2 mediante approcci di genomica e trascrittomica.


CNR-RSTL n.26: Metodi Bayesiani di selezione delle variabili con applicazioni alla genomica.

Regressione non-parametrica e wavelets (2001)

Progetto Giovani ricercatori Università degli Studi di Napoli