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Prof. Dr. Benjamin Paaßen

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Contact

1. Faculty of Technology / Research Groups / Knowledge Representation and Machine Learning

Gruppenleitung

E-Mail
bpaassen@techfak.uni-bielefeld.de  
Phone
+49 521 106-87838  
Phone No. of Secretary
+49 521 106-86303 − Secretary's Details
Office
CITEC 1-220 − Locations Map
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2. Center for Cognitive Interaction Technology CITEC / Associated Research Groups / Knowledge Representation and Machine Learning

E-Mail
bpaassen@techfak.uni-bielefeld.de  
Phone
+49 521 106-87838  
Office
CITEC 1-220 − Locations Map
Add to address-book

3. SFB / Transregio 318 “Constructing Explainability“ / TRR 318 Project Area C: Representing and computing explanations / TRR 318 Subproject C01: Explanations for healthy distrust in large language models

4. Joint Research Center on Cooperative and Cognition-enabled AI

5. Faculty of Technology / Committees and commissions / Prüfungsausschüsse / Intelligente Interaktive Systeme(Master)

Vorsitz
Mitglied
Gruppe der Hochullehrer*innen

6. Faculty of Technology / Committees and commissions / Fakultätskonferenz

Mitglied
Gruppe der Hochschullehrer*innen

E-Mail
bpaassen@techfak.uni-bielefeld.de  
Add to address-book

7. Faculty of Technology / Committees and commissions / Habilitationsausschuss

Mitglied
Gruppe der Hochschullehrer*innen

Module Designee for

Curriculum Vitae

  • Since 2023: Junior Professor for Knowledge Representation and Machine Learning at Bielefeld University.
  • Since 2021: Deputy Head of Research Area "Educational Technology Lab" at the German Research Center for Artificial Intelligence (DFKI)
  • 2021: Postdoc at the Humboldt-University of Berlin
  • 2020: Postdoc at The University of Sydney, Australia
  • 2019: Postdoc at Bielefeld University, Germany
  • 2015-2019: Doctorate/Phd studies at Bielefeld University on 'Metric Learning on Structured Data'.
  • 2013-2015: M.Sc. 'Intelligent Systems' at Bielefeld University.
  • 2010-2013: B.Sc. 'Cognitive Informatics' at Bielefeld University.

Current research topics

  • Knowledge Representation and Machine Learning
  • Machine Learning on Structured Data
  • Interpretable and Explainable Machine Learning
  • Machine Learning for Education