The CREST is a joint laboratory (UMR CNRS 9194) on quantitative methods applied to the social sciences, between CNRS, ENSAE Paris, ENSAI Rennes, the Economics dept. at Ecole polytechnique, and the Economics & Social Science dept. at Télécom Paris.
The i3 (« i-cube ») laboratory is a CNRS joint research unit (UMR CNRS 9217) between Mines Paris, École Polytechnique, and the Economics and Social Science department at Télécom Paris.
The LTCI is accredited for the quality of its partnership-based research as part of Institut Carnot Télécom & Société Numérique, in which Télécom Paris is a member. The lab’s research units have been recognized by the HCÉRES for their excellence, through their « outstanding scientific production in both quality and quantity.”
Contacts
CREST and i3
CNRS joint research units
- David BounieHead of the Economic and Social Sciences DepartmentTélécom Parisemailemail
Information Processing and
Communication Laboratory (LTCI)
- Talel AbdessalemDirector of Research, Director of the LTCITélécom Parisemailemail
Latest news
Ghaya Rekaya wins a Women TechEU grant!
Faculty Members, Start-up — 22/11/2024This prestigious recognition highlights Ghaya and Mimopt Technology's commitment to a more diverse and inclusive technology [...]Indoor and Outdoor Spatial Computing: an awarded project
Design interaction perception, Faculty Members — 16/10/2024The joint project "In-and-Out" between Panos Mavros, Télécom Paris, and Jakub Krukar, [...]"Top 2%": our faculty members among them
Faculty Members — 26/09/2024This ranking of excellence drawn up by Stanford features more than 210,000 researchers out of more than 8 million active scientists [...]Laurie Ciaramella receives a grant for young researcher !
Digital Economy, Faculty Members — 09/07/2024Assistant professor in Economics, she receives an ANR (Agence nationale de la recherche, French Research [...][Ideas] Hybrid, explainable AI for medical imaging
Data Science & AI, Faculty Members — 07/06/2024Isabelle Bloch: Hybrid, explainable AI combines knowledge-based approaches with data-driven learning methods.