Erik J. Bekkers

Associate Professor in Geometric Deep Learning
Based at AMLab (University of Amsterdam)

Leading the Ideal Machine Intelligence research area within AMLab. My work bridges the gap between the elegance of natural laws and the power of artificial intelligence. I focus on Geometric Deep Learning—embedding the symmetries and structures of nature into the learning process. I am also a Research Fellow at New Theory and Director for the ELLIS Program on Geometric Deep Learning.

Biography

I am an Associate Professor in Geometric Deep Learning at the University of Amsterdam (AMLab). My research focuses on grounding artificial intelligence in the rigorous principles of physics and geometry, aiming to build robust representations of the world that bridge the mathematical elegance of nature with modern machine learning.

Beyond my academic role, I serve as a Research Fellow at New Theory and as Director for the ELLIS Program on Geometric Deep Learning.

Before joining the UvA, I worked as a post-doctoral researcher in applied differential geometry at the Technical University Eindhoven (TU/e). I completed my PhD in Biomedical Engineering (cum laude) at TU/e, where I developed medical image analysis algorithms based on sub-Riemannian geometry in the Lie group SE(2)—work inspired by the mathematical principles underlying human visual perception.

I am honored to have received several recognitions, including the MICCAI Young Scientist Award 2018 and two personal research grants from the Dutch Research Council (NWO): a VENI grant (2019) on Context-Aware AI and a VIDI grant (2023) for the project SIGN (Scalable Inference of Geometry-Grounded Neural Representations).

Research Group

I lead the Ideal Machine Intelligence group at AMLab, currently two postdoctoral researchers and thirteen PhD candidates. The group is supported by my NWO VIDI grant SIGN (Scalable Inference of Geometry-Grounded Neural Representations) and an earlier NWO VENI grant, with members hosted across AMLab, the qurAI group, and Amsterdam UMC. I also serve as Director of the ELLIS Program on Geometric Deep Learning.

Postdoctoral Researchers

Georg BökmanAMLab — Scalable Equivariant Architectures
Mina Ghadimi AtighAMLab — Hyperbolic Representation Learning

PhD Candidates

Sharvaree VadgamaAMLab — Geometric Representation Learning & Generative ML
Putri van der LindenAMLab — Geometric Deep Learning & Uncertainty
David WesselsAMLab — Geometric Representation Learning & Histopathology
Alejandro García CastellanosAMLab — Geometry, Invariant Theory & Probabilistic Modeling
Olga ZaghenAMLab — Geometric & Topological Deep Learning
Maria Esteban CasadevallAMLab / ELLIS — Geometric Deep Learning
Friso de KruiffAMLab / CuspAI — Geometric Deep Learning & Materials Science
Cong LiuAMLab / AI4Science — Protein Design & Generative Modeling
Mohammad IslamqurAI — Geometry & Uncertainty in Medical Imaging (Ophthalmology)
Michel BotrosqurAI — AI in Histopathology (Esophageal Adenocarcinoma)
Soufiane Ben HaddouqurAI — Medical Imaging AI
Ylva WeedaAmsterdam UMC — AI in Histopathology, Personalised Immunotherapy
Thijs KuipersAmsterdam UMC — Geometry & Generative AI in Stroke