Chaire IDAML

The objectives of the academic chair

The Industrial Analytics and Machine Learning (IDAML) Chair based at the Centre Borelli of ENS-Paris-Saclay in Gif sur Yvette has the main objective of developing scientific expertise around data science motivated by industrial applications presented by the Chair's partners. 

Since 2017, the IDAML Chair

•  Builds scientific programs allowing the development of research and scientific knowledge on selected themes in collaboration with academic and research partners.
• Welcomes and participates in the training of students, doctoral students and post-doctoral students to make them actors of Artificial Intelligence for industry.
• Leads an international scientific community around machine learning applied to industrial data, notably in the form of conferences and seminars.
• Disseminates research results, through publications and presentations, but also through the production of open source software facilitating the transfer of knowledge and technology to industry.

The main research themes addressed by the Chair

1. « Transfer learning »

Learning by transferring decision rules is the key to accelerating learning on new instances of the system with less training data. The aim is to produce high-performance AND robust rules.

2. « Graph Signal Processing » 

Multivariate signals measured on a graph structure are typical of the temporal and spatial information produced by sensor networks. This is a rapidly developing approach at the crossroads of signal processing, graph theory and machine learning.

3. Monitoring of complex systems

This subject, which is emblematic of industrial cases, is based on the modelling of complex multivariate data for the purpose of detecting/predicting events, which requires, in particular, the fusion of heterogeneous data and taking into account the nature and organization of the system to be monitored.

4. Operational reserach

Operational research: these problems refer to discrete optimization problems, the resolution of which is critical for embedded computing and the optimal allocation of resources in network management.

Partners

• CEA (2016 - .)
• ENSIIE Paris-Evry (2018)
• ATOS (2016-2021)
• Banque de France (2018-2021)
• Bertin Technologies / ChapsVision (2018-2021) 
• Michelin (2018 - .)
• SNCF (2018 - .)