Making AI Ready for Safety-Critical Applications

This project is a collaborative endeavor of researchers (6 supervisors, 3 PhD students and one postdoctoral fellow) which will be either members or visitors of the incoming “International Laboratory on Learning Systems” (ILLS) of the CNRS (starting in early 2022) with Université Paris-Saclay, McGill University and École de technologie supérieure (ETS). We will develop rigorous techniques for building safe and trustworthy AI systems, establishing confidence in their behavior and limitations, thereby facilitating their successful adoption in society. Our research axes will address three fundamental problems: How to detect errors in image segmentation algorithms? How to learn more with less data? How to quantify the information leakage of trained software? We are expected to devise new methods for preventing errors in autonomous cars and medical imaging systems; learning from little information; and auditing privacy risks of AI algorithms. Research activities will be constructed to facilitate the design of truly interdisciplinary research between Computer Sciences, Electrical Engineering and Applied Mathematics, where the potential for innovation is greatest. Our results will be disseminated at the flagship conferences and in prestigious journals of machine learning.

Faculty Supervisor:

Éric Granger;Ismail Ben Ayed;Jose Dolz

Student:

Partner:

CentraleSupélec;École Polytechnique

Discipline:

Computer science

Sector:

Education

University:

École de technologie supérieure

Program: