Postdoc

Erik Bekkers

Science, science is great. I love science. With any luck, it'll save us all. Isaac Brock.

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Group / Unit
Centre for Analysis, Scientific Computing, and Applications
Building
MetaForum
Floor / room
7.074

Research Profile

Erik Bekkers is a postdoctoral researcher in the Mathematical Image Analysis group at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His areas of expertise include differential geometry, machine learning and medical image analysis. With his current research on the mathematical foundations of deep learning he addresses core problems in medical image analysis based on generic mathematical solutions that enable a wide application scope. His research is highly interdisciplinary in nature (from advanced mathematics to applied engineering research and clinical science) which is reflected by his industrial and clinical involvement through collaborations and a previous Ph.D. project (cum laude) carried out in a joint position at medical device company i-Optics B.V. (the Hague, NL) and at Biomedical Engineering (TU/e). For his pioneering work in the field of geometric deep learning he was awarded the prestigious Young Scientist Award at MICCAI, the premier international conference in the field of medical image computing, and the Philips Impact Award at MIDL, the international conference on Medical Imaging with Deep Learning.

Academic Background

Erik Bekkers received his M.Sc. degree in Biomedical Engineering in 2012 at Eindhoven University of Technology (TU/e), the Netherlands. In January 2017 he received his Ph.D. degree (cum laude)  for  his  thesis  "Retinal Image Analysis using Sub-Riemannian Geometry in SE(2)," which was conducted in a combined position at Biomedical Engineering at TU/e and at the medical device company i-Optics B.V., the Hague, the Netherlands. He currently is a postdoctoral researcher at the Department of Mathematics and Computer Science at TU/e, working on the application of Lie group (and control) theory in medical image analysis and machine learning.

Educational Activities

  • Mathematical models in physiology
  • Linear algebra 1

Ancillary Activities

No ancillary activities