PeopleofSuperintelligence

Researcher

Floris Geerts

Floris Geerts is a researcher whose work connects database theory with graph neural networks. He earned a master's degree in Mathematics at Ghent in 1995 and a doctorate in Computer Science at Hasselt in 2001, then joined the University of Antwerp as Research Professor in 2011 after research posts in Helsinki and Edinburgh. In 2015, he shared the ACM PODS Mendelzon Test-of-Time Award, and in 2022 he and Juan Reutter received an ICLR Outstanding Paper Award for GNN expressivity. His papers characterized GNNs with local graph parameters in 2021, examined links between expressivity and generalization in 2024 and 2025, and studied algorithm learning in 2026. As of October 2026, he is a professor in Antwerp's Department of Computer Science and guides Adrem Data Lab research on data management and graph learning.

Timeline

  1. 1995–2011

    From Ghent degrees to Antwerp

    At the University of Ghent, he earns an M.Sc. in Mathematics in 1995; at the University of Hasselt, he earns a Ph.D. in Computer Science in 2001. He takes a postdoctoral post at the University of Helsinki and spends seven years as a Senior Research Fellow at the University of Edinburgh before joining the University of Antwerp as Research Professor in 2011.

  2. 2015

    A PODS test-of-time award

    He shares the 2015 ACM PODS Alberto O. Mendelzon Test-of-Time Award with Michael Benedikt and Wenfei Fan for 'XPath satisfiability in the presence of DTDs,' published in the PODS proceedings in 2005.

  3. 2021

    Local graph parameters for GNNs

    In 2021, he coauthors 'Graph Neural Networks with Local Graph Parameters' with Pablo Barceló, Juan Reutter and Maksimilian Ryschkov. The paper characterizes the distinguishing power of these GNNs using a variant of the Weisfeiler–Leman test and graph structural properties.

  4. 2022

    An ICLR Outstanding Paper Award

    He and Juan L. Reutter receive an ICLR 2022 Outstanding Paper Award for 'Expressiveness and Approximation Properties of Graph Neural Networks.' The paper views GNNs as expressions in a tensor language and derives bounds on their separation power using Weisfeiler–Leman tests.

  5. 2023

    Simons visit and Karp lecture

    He is a visiting scientist in Fall 2023 in the Simons Institute program Logic and Algorithms in Database Theory and AI. In 2023, he gives the Richard M. Karp Distinguished Lecture 'The Power of Graph Learning,' using connections between graph learning and database theory to analyze the expressive power of graph learning methods.

  6. 2024

    Expressivity and generalization

    In 2024, he coauthors 'Weisfeiler-Leman at the margin: When more expressivity matters' with Billy J. Franks, Christopher Morris and Ameya Velingker. The paper finds that a GNN architecture's expressivity offers limited insight into generalization performance when viewed through graph isomorphism. In August 2024, he gives an Oxford seminar titled 'Expressiveness and Generalisation in Graph Neural Networks.'

  7. 2025

    GNN generalization theory at ICLR

    In 2025, he coauthors 'Towards Bridging Generalization and Expressivity of Graph Neural Networks' with Shouheng Li, Dongwoo Kim and Qing Wang for ICLR. The paper connects GNN generalization to the variance in graph structures they capture and proposes a margin-based generalization bound.

  8. As of October 2026

    Professor in Antwerp

    As of October 2026, he is a professor in the University of Antwerp's Department of Computer Science and part of Adrem Data Lab, where he guides research on the principles of data management and graph learning. In 2026, he coauthors 'Which Algorithms Can Graph Neural Networks Learn?' for ICML, which gives conditions for message-passing GNNs to learn tasks such as single-source shortest paths and minimum spanning trees.

Career

  1. Since 2011University of AntwerpResearch Professor
  2. 2023University of California, BerkeleyVisiting Scientist
  3. Until 2011University of EdinburghSenior Research Fellow

Education

  1. 2001Hasselt UniversityPh.D. in Computer Science
  2. 1995Ghent UniversityM.Sc. in Mathematics

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Floris Geerts. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Antwerp. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of California, Berkeley. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  4. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of California, Berkeley. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Floris Geerts yet. Send us a free-licensed photo.

  1. Floris Geerts simons.berkeley.edu. Checked 22 October 2026.
  2. Michael Benedikt wins ACM PODS Alberto O. Mendelzon Test-of-Time Award cs.ox.ac.uk. Checked 22 October 2026.
  3. Floris Geerts fgeerts.github.io. Checked 22 October 2026.
  4. [2106.06707] Graph Neural Networks with Local Graph Parameters arxiv.org. Checked 22 October 2026.
  5. Announcing the ICLR 2022 Outstanding Paper Award Recipients – ICLR Blog blog.iclr.cc. Checked 22 October 2026.
  6. [2204.04661] Expressiveness and Approximation Properties of Graph Neural Networks arxiv.org. Checked 22 October 2026.
  7. The Power of Graph Learning | Richard M. Karp Distinguished Lecture simons.berkeley.edu. Checked 22 October 2026.
  8. [2402.07568] Weisfeiler-Leman at the margin: When more expressivity matters arxiv.org. Checked 22 October 2026.
  9. Expressiveness and Generalisation in Graph Neural Networks cs.ox.ac.uk. Checked 22 October 2026.
  10. Towards Bridging Generalization and Expressivity of Graph Neural Networks proceedings.iclr.cc. Checked 22 October 2026.
  11. Which Algorithms Can Graph Neural Networks Learn? proceedings.mlr.press. Checked 22 October 2026.