The "decision" axis aims to help one or more individuals, confronted with complex decision alternatives, possibly described by multiple conflicting consequences, to make decisions by providing them appropriate support tools.
This axis currently includes the following research areas: modelling and the elicitation of preferences, multi-criteria decision aiding, optimisation, mathematical programming, heuristics, metaheuristics and matheuristics.
The "data" axis deals with the extraction of knowledge from large quantities of data (either structured, or partially structured) via automatic or semi-automatic methods.
This axis currently includes the following research areas : data mining (unsupervised and supervised learning), text mining, graph mining, and data visualisation.
The "information" axis focuses on the agregation and the quality of data and information originating from heterogeneous sources.
More specifically, the team is interested in various measures of the quality of this aggregated information, in the relevance of the descriptors of visual content, in the quality of the information in accordance with the usage, and in the caracterization and the generalization of algorithmic properties of quality measures.