Grundare av "Representation, Learning and Planning lab"

Hector Geffner är gäst-Wallenbergprofessor vid Institutionen för datavetenskap (IDA) inom Artificiell intelligens och integrerade datorsystem (AIICS), där han grundade "Representation, Learning and Planning lab".

Hectors forskning handlar om meningsfulla modeller från data och att få databaserade och modellbaserade komponenter att komplettera, förbättra och informera varandra, i samband med agerande och planering.

Korta fakta om Hector Geffner 

(På engelska)

  • Hector Geffner is an Alexander von Humboldt Professor at the RWTH Aachen University, Germany, since January 2023, and a Guest Wallenberg Professor at Linköping University, Sweden, since September 2019.
  • Before joining RWTH Aachen, he was an ICREA Research Professor at the Universitat Pompeu Fabra (UPF) in Barcelona, Spain, since 2001.
  • Hector obtained a Ph.D. in Computer Science at UCLA in 1989 and then worked at the IBM T.J. Watson Research Center in NY, and at the Universidad Simon Bolivar in Caracas.
  • Hector is a Fellow of AAAI and EurAI, and former Associate Editor of AI and JAIR.
  • His most recent book, with Blai Bonet, is “A Concise Introduction to Models and Methods for Automated Planning”, Morgan and Claypool, 2013.
  • His research interests are in computational models of reasoning, action, and learning that are effective and general.
  • Hector received the 1990 ACM Dissertation Award and is best known for his work in planning for which he received the 2009, 2010, and 2014 ICAPS Influential Paper Awards.


  • Currently, he is interested in methods for learning representations for acting and planning, and leads an ERC project in the area (RLeap, 10/2020-10/2025).
  • He teaches courses on AI and on social and technological change.
  • Prof. Geffner's current research is aimed at addressing the problem of learning representations from data that support reasoning, reuse, and generalization. It is a central problem in AI where deep learning yields inflexible, black boxes that cannot be trusted, and model-based approaches yield flexible and reusable behaviors but relying on handcrafted representations.




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