One of our core research topics lies at the intersection between stochastic optimization and machine learning.  This includes studying the behavior of stochastic optimization algorithms when optimizing non-convex optimization landscapes such as the ones encountered in deep learning models,  as well as designing and analyzing new stochastic optimization methods, with a special interest for applications in the field of machine learning.

Deep Learning has become a key technology solving complex problems, such as beating humans at complex games (Silver et al., 2016), driving cars autonomously (Bojarski et al., 2016), or folding proteins (Senior et al., 2020). However, Deep Learning models are still not well-understood from a theoretical perspective. Our goal is to deepen our understand of such models using advanced mathematical tools from areas such as probability theory, random matrix theory, optimization, etc.