ChairesIA_2019_1 - Chaires de recherche et d'enseignement en Intelligence Artificielle - vague 1 de l'édition 2019

TopAI: Topological Data Analysis for Machine Learning and AI – TopAI

Submission summary

TopAI is a project that aims at developing a world-leading research activity on topological and geometric approaches in Machine Learning (ML) and Artificial Intelligence (AI) going from mathematical foundations to industrial applications with high societal and economic impact in personalized medicine and AI-assisted medical diagnosis.
Motivated by a strong interest in the understanding of the complex structures underlying data, geometry and topology have recently experienced important developments towards data analysis and Machine Learning. New mathematically well-founded theories gave birth to the field of Topological Data Analysis (TDA), which is now arousing interest from both academia and industry. During the last few years, TDA has witnessed many successful theoretical contributions, important algorithmic and software developments, and, some real-world successful applications. These developments have demonstrated very promising potential in the combination of TDA methodology with other ML and AI approaches, opening new theoretical and applied research directions at the crossing of TDA, ML and AI that are at the core of the TopAI project.
The TopAI activities are organized around a double academic and industrial/societal objective. First, TopAI aims at designing new mathematically well-founded topological and geometric methods and tools for Data Analysis and ML and to make them available to the data science and AI community through state-of-the-art software. Second, thanks to already established close collaborations and the strong involvement of two French innovative SMEs, Sysnav and MetaFora, TopAI aims at exploiting its expertise and tools to address a set of challenging problems with high societal and economic impact in personalized medicine and AI-assisted medical diagnosis.
TopAI embraces a unique variety of expertise in a common framework going from the mathematical and foundations of TDA and AI to applied and industrial research. This combination of upstream and downstream research will create a unique synergy whose expected outcomes include academic and basic research contributions, industrial transfer and valorization.

Project coordination

CHAZAL frédéric (Centre de Recherche Inria Saclay - Île-de-France)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


Inria Saclay - Ile de France - équipe DATASHAPE Centre de Recherche Inria Saclay - Île-de-France

Help of the ANR 546,951 euros
Beginning and duration of the scientific project: August 2020 - 48 Months

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