ADAM Team

The intersection of Big Data and artificial intelligence has led to considerable advances, the scientific and societal impact of which is still in its infancy. The themes of the ADAM team are situated in these two fields and at their intersection. The team is interested in research questions where data is at the heart, whether to organize it, query it, integrate and enrich it, extract rules from it, integrate knowledge into it, or extract knowledge from it using various and varied learning methods. The team's wide-ranging expertise enables it to address a number of scientific and technical challenges, particularly with regard to unstructured data, time series, high-dimensional data and, more generally, complex data.  Its research is structured around three main axes: integration and enrichment of irregular data, which is part of the field of symbolic AI; machine learning from complex and/or imperfect data; spatial and/or temporal data mining.

These themes are fueled by collaborative projects, often interdisciplinary, with applications mainly in the smart city, industry and healthcare sectors.

• GoGreen ROUTES – H2020
• MASTER – H2020
• i-RECORDS – Era PerMed
• RHU RECORDS – ANR
• IHU PROMETHEUS – ANR
• ACOMI – ANR
• Collaborations BPCE & SAFRAN – ANRT
• AI4IDF – Région Île-de-France
• FL4Mobility – Labex DIGICOSME – ESIEA
• CLEMI – MSH – ParisSaclay

• INRIA
• ICS-FORT
• HPC Lab, ISTI, CNR
• Universidade Federal do Ceará
• ESIEA
• BPCE
• Safran

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