"Science is but a perversion of itself unless it has as its ultimate goal the betterment of humanity."

Nikola Tesla

"Maybe the journey isn't so much about becoming anything.

Maybe it's about unbecoming everything that isn't you so you can be who you were meant to be in the first place."


I am a Postdoc at the Robinson lab at the Institute of Molecular Life Sciences at the University of Zurich where I am working on the development of cutting-edge statistical methods in bioinformatics, mostly for bulk and single-cell RNA-seq data. In particular, I am currently focusing on two methodological projects. The first one consists in developing a Bayesian hierarchical model to identify differentially spliced genes, via differential transcript usage (DTU), from bulk RNA-seq data. The second project aims at creating a methodology, based on permutation tests, to perform differential state analyses from single-cell RNA-seq data.

Previously, I was a PhD student at the Department of Statistics at the University of Warwick, where I worked on Bayesian hierarchical models to investigate stochastic systems in single cells.

In general terms, my interests are quite broad and lie in the development of statistical methods for applications in the medical and biological fields.

Research positions


Thesis: "Bayesian Hierarchical Stochastic Inference on Multiple, Single Cell, Latent States from both Longitudinal and Stationary Data".

Supervisor: Prof Barbel Finkenstadt.

Dissertation: "A composite likelihood approach to predict the babies sex".

Supervisor: Prof. Bruno Scarpa. Co-supervisor: Prof. Nicola Sartori.

Dissertation: "A logistic analysis to predict the axillary lymph node status, in patients affected by breast cancer and with positive sentinel lymph node".

Supervisor: Prof. Maria Grazia Pittau.



  • Koen Van Den Berge​*, Katharina Hembach​*, Charlotte Soneson​*, Simone Tiberi*, Lieven Clement, Michael I Love, Rob Patro, Mark Robinson.

RNA sequencing data: hitchhiker's guide to expression analysis.

Annual Review of Biomedical Data Science (2019). *joint first authors.

  • Anthony Lee, Simone Tiberi and Giacomo Zanella.

Unbiased approximations of products of expectations.

Biometrika (2019).

  • Simone Tiberi, Mark Walsh, Massimo Cavallaro, Daniel Hebenstreit and Bärbel Finkenstädt.

Bayesian inference on stochastic gene transcription from flow cytometry data.

Bioinformatics (2018).

  • Simone Tiberi, Bruno Scarpa and Nicola Sartori.

A composite likelihood approach to predict the sex of the baby.

Statistical Methods in Medical Research (2018).

  • Augusto Lombardi, Stefano Maggi, Marzia Lo Russo, Francesco Scopinaro, Domenica Di Stefano, Maria Grazia Pittau, Simone Tiberi and Claudio Amanti.

Non-sentinel lymph node metastases in breast cancer patients with a positive sentinel lymph node: validation of five nomograms and development of a new predictive model.

Tumori (2011).


  • Massimo Cavallaro, Mark Walsh, Matt Jones, James Teahan, Simone Tiberi, Bärbel Finkenstädt and Daniel Hebenstreit.

Polymerase recycling contributes to transcriptional noise.

A pre-print is available at: https://www.biorxiv.org/content/early/2019/01/09/514174.

In preparation

  • Simone Tiberi et al.

Bayesian hierarchical stochastic modelling, via the Diffusion Approximation, of discrete time single cell Nrf2 oscillations.

  • Simone Tiberi et al.

BANDITS: a Bayesian hierarchical model for differential splicing accounting for sample-to-sample variability and mapping uncertainty.

  • Christian Sailer, Simone Tiberi et al.

Apomixis alone does not confer a competitive advantage.



Bioconductor R packages

  • BANDITS: Bayesian ANalysis of DIfferenTial Splicing

BANDITS is a Bayesian hierarchical model for detecting differential splicing, of both genes and transcripts, while accounting for sample-to-sample variability and mapping uncertainty.

BANDITS is available on Bioconductor (https://bioconductor.org/packages/BANDITS) and on github (https://github.com/SimoneTiberi/BANDITS).


Oral presentations


  • BITS Bioinformatics Italian Society Meeting 2019, Palermo, 26-28 June 2019.
  • IBS Channel Network Conference 2019, Rothamsted Research, 10-12 July 2019.
  • ISMB/ECCB 2019, Basel, 21-25 July 2019.

Conferences and workshops

  • IBC International Biometric Conference 2018. Barcelona, 8-13 July 2018.
  • BC2 Basel Computational Biology Conference 2017. Basel, 12-15 September 2017.
  • IBS Channel Network Conference 2017. Hasselt University, 24-26 April 2017.

Seminars and meetings

  • IMLS Research Progress Report, University of Zurich, 20 April 2018.
  • BIO612 - Seminars in Bioinformatics, University of Zurich, 18 April 2018.
  • Young research meeting (YRM), The University of Warwick,
    • 8 March 2016
    • 28 March 2014
    • 19 November 2013
    • 23 September 2013
  • Postgraduate Open Day, The University of Warwick,
    • 25 November 2015
    • 26 November 2014


Material available at: https://github.com/markrobinsonuzh/pretoria_rnaseq_course_feb2019.

Theory and methods of RNA-seq studies: material available at: https://github.com/SimoneTiberi/BG4-2018.

The University of Zurich

  • Academic year 2016/2017, Fall Semester: STA121 - Statistical Modelling.

The University of Warwick

During my PhD at Warwick I taught several tutorials in the following modules:

  • Academic year 2015/2016, Term 1: ST301/ST413 - Bayesian Statistics & Decision Theory
  • Academic year 2015/2016, Term 1: ST333/ST406 - Applied Stochastic Processes
  • Academic year 2014/2015, Term 2: ST219 - Mathematical Statistics Part B
  • Academic year 2014/2015, Term 1: ST218 - Mathematical Statistics Part A
  • Academic year 2013/2014, Term 2: ST115 - Introduction to Probability
  • Academic year 2013/2014, Term 1: ST116 - Mathematical Techniques