Senior researcher at IDSIA, member of the Imprecise Probability Group.

Research interests

Probabilistic Graphical Models

Machine Learning

Bayesian Hypothesis Testing  and Bayesian Hierarchical Models

Imprecise Probability and Credal Classification



 My list of Publications

 My Google Scholar page.


Best Paper Prize  at the International Environmental Modelling and Software Conference 2002 (Lugano, Switzerland).

Scientific events: PGM 2016 and ISIPTA 2017

I  co-chaired and co-organized PGM 2016, the Eighth International Conference on Probabilistic Graphical Models (Lugano,  September 2016). 
The  proceedings of PGM 2016 have been published within the series of the Proceedings of Machine Learning Research.

I  co-chaired and co-organized ISIPTA 2017, the Tenth International Symposium on Imprecise Probability: Theories and Applications (Lugano,  July 2017). 
The  proceedings of ISIPTA 2017 have been published within the series of the Proceedings of Machine Learning Research.

Program committees 

AAAI (2018):  AAAI Conference on Artificial Intelligence

IJCAI (2015, 2016, 2018): Int. Joint Conference on Artificial Intelligence

UAI (2016): Conference on Uncertainty in Artificial Intelligence

PGM (2014, 2016, 2018): European Workshop on Probabilistic Graphical Models

ECAI (2014, 2016, 2018): European Conference on Artificial Intelligence

ISIPTA: International Symposium on Imprecise Probability: Theories and Applications: ( 2009, 2011, 2013, 2015, 2017)

DMIN: International Conference on Data Mining (2011 , 2012 , 2013 , 2014 )


Reviewer for Machine Learning,  Int. J. of Approximate ReasoningInt. J. of Artificial Intelligence Research

Outstanding reviewer certificate  from  Environmental Modelling and Software


Applied Statistics: Bach. of Management Engineering (SUPSI). Main topics: statistical inference and statistical process control.

Uncertain Reasoning and Data Mining:  Master of Science in Engineering (SUPSI). Main topics: Bayesian networks and data mining. Co-teacher.


Bayesian hypothesis testing in machine learning

Slides and code of our tutorial at ECML/PKDD 2016:

G. Corani, A. Benavoli, J. Demsar

Comparing competing algorithms: Bayesian versus frequentist hypothesis testing.

Naive Credal Classifier

The open source implementation of the naive credal classifier is the JNCC2.

Credal Model Averaging 

The R implementation of credal model averaging for logistic regression is due to A. Mignatti. The algorithms are discussed in these two papers (link1, link2). The software and the marmot data set used in those papers is available here.