"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."
Paulo Coelho

I saw the angel in the marble and carved until I set him free."
Michelangelo Buonarroti

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. Previously, I was a PhD student at the Department of Statistics at the University of Warwick, where I focused on stochastic Bayesian hierarchical models to investigate transcription in single cells.
In general terms, my interests are broad and lie in the development and application of statistical methods in computational biology.

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.


  1. Simone Tiberi, Helena L Crowell, Pantelis Samartsidis, Lukas M Weber and Mark D Robinson.
    distinct: a novel approach to differential distribution analyses.
    To appear in The Annals of Applied Statistics (2022+).
    Also on bioRxiv (2020): https://doi.org/10.1101/2020.11.24.394213.

  2. Philipp Weiler, Koen Van den Berge, Kelly Street*, Simone Tiberi*.
    A guide to trajectory inference and RNA velocity.
    To appear in
    Methods in Molecular Biology (2022+). *joint last authorship.
    on biorxiv (2021): https://doi.org/10.1101/2021.12.22.473434.

  3. Rachel M. Miller, Ben T. Jordan, Madison M. Mehlferber, Erin D. Jeffery, Christina Chatzipantsiou, Simi Kaur, Robert J. Millikin, Yunxiang Dai, Simone Tiberi, Peter J. Castaldi, Michael R. Shortreed, Chance John Luckey, Ana Conesa, Lloyd M. Smith, Anne Deslattes Mays, Gloria M. Sheynkman.
    Enhanced protein isoform characterization through long-read proteogenomics.
    Genome Biology (2022).

  4. Christian Sailer, Simone Tiberi, Bernhard Schmid, Juerg Stoecklin and Ueli Grossniklaus.
    Apomixis and genetic background affect distinct traits in Hieracium pilosella L. grown under competition.
    BMC Biology (2021).

  5. Massimo Cavallaro, Mark Walsh, Matt Jones, James Teahan, Simone Tiberi, Bärbel Finkenstädt and Daniel Hebenstreit.
    3’-5’ crosstalk contributes to transcriptional bursting.
    Genome Biology (2021).

  6. Simone Tiberi and Mark D Robinson.
    BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty.
    Genome Biology (2020).

  7. 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 authorship.

  8. Anthony Lee, Simone Tiberi and Giacomo Zanella.
    Unbiased approximations of products of expectations.
    Biometrika (2019).

  9. Simone Tiberi, Mark Walsh, Massimo Cavallaro, Daniel Hebenstreit and Bärbel Finkenstädt.
    Bayesian inference on stochastic gene transcription from flow cytometry data.
    Bioinformatics (2018).

  10. Simone Tiberi, Bruno Scarpa and Nicola Sartori.
    A composite likelihood approach to predict the sex of the baby.
    Statistical Methods in Medical Research (2018).

  11. 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).



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).

  • distinct: a method for differential analyses via hierarchical permutation tests

distinct is a statistical method to perform differential testing between two or more groups of distributions. Unlike most methods for differential expression, distinct identifies both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean).

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


Oral presentations

Conferences and workshops

  • Bioconductor Conference 2021, virtual, 4-6 August 2021.

  • European Young Statisticians Meetings 2021, virtual, 6-10 September 2021.

  • BC2 Basel Computational Biology Conference 2021. Basel, 13-15 September 2021.

  • European Bioconductor Meeting 2020, virtual, 14-18 December 2020. Slides available at: https://f1000research.com/slides/9-1450.

  • ISMB/ECCB 2019, Basel, 21-25 July 2019. Slides available at: https://f1000research.com/slides/8-1223.

  • IBS Channel Network Conference 2019, Rothamsted Research, 10-12 July 2019.

  • BITS Bioinformatics Italian Society Meeting 2019, Palermo, 26-28 June 2019.


  • 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.


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.

  • University of Zurich:

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

  • University of Warwick (tutorials):

    • 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