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.
Hector Geffner
Gästprofessor
Grundare av "Representation, Learning and Planning lab".
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".
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- 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.
Publikationer
2023
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2022
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