This group develops optimization and machine learning methods for the analysis and processing of complex data. Research topics include convex and nonconvex optimization, signal reconstruction, supervised and unsupervised learning, robustness to noise, and applications to dynamic systems and networks.
The research combines theoretical contributions with algorithmic development to address real-world applications.
Members:
- Marc Castella
- Clément Fernandes
- Baptiste Goujaud
- Yohan Petetin
- Sholom Schechtman (group leader)
