PeopleofSuperintelligence

Researcher

Pierre Geurts

Pierre Geurts is a professor of machine learning at the University of Liège who developed the Extra-Trees ensemble method with Damien Ernst and Louis Wehenkel. He earned an engineering degree in 1998 and a doctorate in applied sciences in 2002 under Wehenkel, then held a CNRS postdoctoral position at the University of Evry from 2005 to 2007. Extremely Randomized Trees appeared in Machine Learning in 2006 and describes a method whose main strength is computational efficiency. GENIE3, a tree-based method for inferring gene regulatory networks that he coauthored, was the best performer in the DREAM4 In Silico Multifactorial challenge in 2010, and in 2016 he coauthored the Cytomine paper on collaborative analysis of multi-gigapixel imaging data. He is a professor in the EECS department of the Montefiore Institute and teaches introduction to machine learning with Wehenkel.

Now at
Montefiore Institute

Timeline

  1. 1998–2002

    Engineering degree and doctorate

    He earns a civil engineering degree in electrical engineering (computer science) in 1998 and a doctorate in applied sciences in 2002. His thesis, supervised by Louis Wehenkel at the Montefiore Institute, covers decision tree induction, the bias/variance tradeoff and time series classification.

  2. 2005

    Tree-based batch reinforcement learning

    In 2005, he coauthors "Tree-Based Batch Mode Reinforcement Learning" with Damien Ernst and Louis Wehenkel. The paper tests tree-based supervised learning methods, including the newly proposed extremely and totally randomized trees, for extracting an optimal control policy from observed four-tuples.

  3. 2005–2011

    CNRS postdoc and FNRS researcher

    From 2005 to 2007, he holds a CNRS postdoctoral position at the University of Evry in France. From 2006 to 2011, he is a qualified researcher of the FNRS in Belgium.

  4. 2006

    Extremely randomized trees

    In 2006, he publishes "Extremely Randomized Trees" in Machine Learning with Damien Ernst and Louis Wehenkel. The method randomizes strongly both attribute and cut-point choice while splitting a tree node, and its main strength is computational efficiency.

  5. 2010

    GENIE3 for regulatory networks

    In 2010, he coauthors GENIE3 with Vân Anh Huynh-Thu, Alexandre Irrthum and Louis Wehenkel. The algorithm predicts each gene's expression from all other genes with Random Forests or Extra-Trees to infer gene regulatory networks, and it is the best performer in the DREAM4 In Silico Multifactorial challenge.

  6. 2016

    The Cytomine imaging platform

    In 2016, he coauthors the Bioinformatics paper "Collaborative analysis of multi-gigapixel imaging data using Cytomine" with Raphaël Marée, Louis Wehenkel and others. Cytomine is open-source web software with machine learning that the Montefiore research unit in Liège initiated in 2010.

  7. As of October 2026

    Professor at the Montefiore Institute

    He is a professor in the EECS department of the University of Liège (Montefiore Institute) and teaches Introduction to machine learning with Louis Wehenkel in 2026–2027. His research covers the design and analysis of machine learning algorithms, with an emphasis on scalability, interpretability and ease of use, and applications in computational and systems biology, computer vision and digital humanities.

Career

  1. NowMontefiore InstituteProfessor
  2. 2006–2011National Fund for Scientific ResearchQualified researcher
  3. 2005–2007CNRSPostdoctoral researcher
  4. 2005–2007Université d'Évry Val-d'EssonnePostdoctoral researcher

Education

  1. 2002University of LiègePhD

From the biography.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes CNRS. 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.
  2. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes CNRS. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  3. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes CNRS. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.
  4. The top 10 organizations employed 22% of people shaping AI in 2025: 51% of company researchers and 21% of university researchersIncludes CNRS. We estimate the share of people shaping AI who worked at the top 10 organizations each year from 1955 to 2025, comparing concentration across companies, universities, and institutes and labs.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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

  1. Pierre Geurts | TRAIL Factory factory.trail.ac. Checked 22 October 2026.
  2. ORBi: Contributions to decision tree induction: bias/variance tradeoff and time series classification - 2002 orbi.uliege.be. Checked 22 October 2026.
  3. Tree-Based Batch Mode Reinforcement Learning jmlr.org. Checked 22 October 2026.
  4. Extremely Randomized Trees | ML Anthology mlanthology.org. Checked 22 October 2026.
  5. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods ncbi.nlm.nih.gov. Checked 22 October 2026.
  6. Cytomine Research @ ULiège uliege.cytomine.org. Checked 22 October 2026.
  7. ELEN0062-1 programmes.uliege.be. Checked 22 October 2026.