Research Focus and Collaborators

Our research spans theory, computational methods, and applications. The primary theoretical objective is to understand the behavior and limitations of current machine learning models through the prism of geometry. This informs the computational objective to develop new, efficient, and better-interpretable models (including graph neural networks, equivariant architectures, neural differential equations, and geometric generative models) with performance guarantees derived from their geometric structure, providing predictions that are not just accurate but also physically plausible.

Our applications are mainly in AI for science, with a particular focus on problems in biochemistry and structural biology, such as molecular simulations, protein design, and drug discovery. We believe science is a collaborative endeavor and work with some of the leading experts in the field. In particular, with our life sciences collaborators, we take the next step in our machine-learning research by testing designed molecules in the wet lab, aiming to uncover new insights into fundamental biology and to help create new medicines.

Collaborations
• Bruno Correia (EPFL)
• Pierre Vandergheynst (EPFL)
• Pietro Liò (Cambridge)
• Xiaowen Dong (Oxford)
• Haggai Maron (Technion/Nvidia)

Candidate’s Profile and Skills

We are looking for PhD students with a strong machine learning background interested in applications in the life sciences domains. Particular topics of interest are geometric generative models, physics-inspired neural networks, and graph neural networks, protein and small molecule design.