Chair objectives

The main objectives of this Chair is to develop and to put in action upstream applied mathematical tools (optimization tools, non Euclidean geometry, global sensitivity analysis, random matrices, ..), for the understanding of machine learning algorithms.

A special emphasis is put on the strong interactions with our industrial partners. Our wish is to work in a virtuous loop, feeding our upstream researches in applied mathematics by solving concrete industrial problems proposed by our industrial collaborators. The addresed problems range from explicability (using for example tools of global sensitivity analysis), the study of neural nets (using for example random matrices or continuous embedding) to the design of new algorithms (using geometric tools).

Programs: Acceptable and certifiable AI
Themes: learning with little of complex data, fair learning, AI and physical models

Chair holder:

Fabrice Gamboa, Institut de Mathématiques de Toulouse / https://www.math.univ-toulouse.fr/~gamboa

Senior researchers :

Reda Chhaibi (UT3, IMT)

Thomas Pellegrini (UT3, IRIT)

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