Chair objectives

This chair aims to analyze the transformations performed by model based and by data based diagnosis methods, deriving mutual benefits, and integrating them closely.

The first objective is to highlight and understand the correspondences that may exist between them and how they could synergize each other.

The second objective is to be able to formulate abstractions of data classifiers that we can map to symbolic or analytical models suitable for diagnosis reasoning, gaining better explainability and acceptability.

This chair is part of the ANITI “Collaborative AI” research and deals with diagnosis, including in the broadest sense monitoring, anomaly detection, fault isolation and identification, prognosis and health management.

These are tasks for which integrated human/AI systems show better performance.

Programs: Acceptable, certifiable & collaborative AI
Themes: Automated reasoning and decision making, explainability, data and anomaly

Chair holder:  Louise Travé-Massuyès, DR CNRS, LAAS

Co-chairs:

Elodie Chanthery, Assistant professor, LAAS-CNRS, INSA, Toulouse

Xavier Pucel, Research engineer, ONERA, Toulouse 

Nathalie Barbosa Roa, Data scientist and Big Data engineer, Vitesco Technologies, Toulouse

Website
http://homepages.laas.fr/louise

Know more

Louise Travé-Massuyès, virtuoso of automatic diagnosis

The researcher devotes her career to researching the causes of machine malfunctions. It develops automatic diagnostic models. A theme that is increasingly based on artificial intelligence techniques.

Read the article in French

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