Full Professor

Mark de Berg

Geometry is everywhere. By my research I want to contribute to a deeper understanding of the techniques needed to tackle the algorithmic challenges involved in processing spatial data in an efficient and reliable manner.

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Group / Unit
Algorithms and Visualization W&I
Building
MetaForum
Floor / room
4.146

Research Profile

Mark is a full professor at Eindhoven University of Technology (TU/e) and chair of the TU/e Algorithms Group. This group performs research in various areas within algorithms, including computational geometry and FPT algorithms, and is inspired by applications in complex networks, geographic information science and design and manufacturing. Mark’s personal interest is in algorithms and data structures in general, and in algorithmic questions involving spatial data in particular. His current research includes algorithms for geometric networks, exact algorithms for NP-hard geometric problems, and spatial data structures. Much of his work centers around the question: How can we exploit the properties that real-world spatial data sets often possess to obtain provably efficient solutions?

Academic Background

Mark de Berg received an MSc in computer science from Utrecht University (the Netherlands) in 1988, and a PhD from the same university in 1992. He is (co-)author of two books on computational geometry, one of which has become the standard textbook in the field. Mark has published over 225 papers in journals and peer-reviewed conferences, and he was on the program committee of many international conferences in the field. He currently serves on the editorial board of three international journals, and on the Computational-Geometry Steering Committee. He is received several prestigious grants, including a VICI grant from NWO, and he is a PI in the Gravitation Program Networks.

Educational Activities

  • Capita selecta algorithms
  • Advanced algorithms
  • Algorithmic Aspects of Data Analysis
  • DBL Algorithms
  • Research methods in computer science
  • Teachers Information Data Science

Ancillary Activities

No ancillary activities