Isabelle Augenstein
Professor
I am a full professor at the University of Copenhagen, Department of Computer Science, where I head the Copenhagen Natural Language Understanding research group as well as the Natural Language Processing section. I am also a co-lead of the Pioneer Centre for Artificial Intelligence. My main research interests are fact checking, low-resource learning and explainability.
Before starting a faculty position, I was a postdoctoral research associate in Sebastian Riedel's UCL Machine Reading group, mainly investigating machine reading from scientific articles. Prior to that, I was a Research Associate in the Sheffield NLP group, a PhD Student in the University of Sheffield Computer Science department, a Research Assistant at AIFB, Karlsruhe Institute of Technology and a Computational Linguistics undergraduate student at the Department of Computational Linguistics, Heidelberg University.
I currently hold a prestigious ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking', as well as the Danish equivalent of that, a DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media'. I am a member of the Danish Young Academy, a unit under the Royal Danish Academy of Sciences and Letters. I am also the Vice President of SIGDAT which organises the EMNLP conference series, a co-founder of Widening NLP (WiNLP), and maintain the BIG Directory of members of underrepresented groups and supporters in Natural Language Processing.
For more information, please see my personal website.
ID: 180388519
Most downloads
-
1527
downloads
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Research output: Contribution to conference › Paper › Research
Published -
354
downloads
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Published -
237
downloads
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
Research output: Book/Report › Book › Research › peer-review
Published