PhD in Optimization

Deep learning has been a driving force behind the rapid progress of AI for more than a decade, culminating recently in the success of large language models powered by transformer architectures—a class of deep neural networks. In parallel, bilevel optimization has emerged as a powerful framework for modeling complex machine learning tasks, giving rise to what we refer to as deep bilevel learning. This PhD project will investigate the mathematical foundations of deep bilevel learning, with the goal of uncovering structural properties that can be exploited to design more efficient, robust, and explainable learning algorithms. The results have the potential to influence the next generation of AI systems and advance theory at the intersection of optimization, machine learning, and deep learning.