Latest news from the Visual Analytics Research Group

We are always looking for student assistants to support our research and teaching activities. If you are interested, please contact Prof. Ewerth.


Best Student Paper Award at JCDL 2022

The paper "Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers" by Arthur Brack, Anett Hoppe, Pascal Buschermöhle, and Ralph Ewerth won the Best Student Paper Award at the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2022)


We are currently offering several PhD scholarships in the PhD networks LernMINT (LearnSTEM) and Responsible AI!

PhD positions in LernMINT:

PhD position in Responsible AI:

If you are interested, please contact Prof. Ewerth.


Nomination for Best Full Paper Award at ICALT 2020

The paper "A Recommender System For Open Educational Videos Based On Skill Requirements" by Mohammadreza Tavakoli, Sherzod Hakimov, Ralph Ewerth, and Gabor Kismihok has been nominated for competing the Best Full Paper Award at the 20th IEEE International Conference on Advanced Learning Technologies (ICALT 2020). 


 

Scientists and researchers needed for regular contributions to the ORKG – Applications can be submitted until 31 May 2021


60-minute introduction to the Open Research Knowledge Graph, its main ideas and potential usage


we would briefly and clearly explain the TIB's diverse range of topics?


An interview: Prof. Dr Ralph Ewerth on his research on learning with and from artificial intelligence


From scholarly communication to data treasures, digital preservation of species and personalised medicine to Open Access


ICMR 2020: Contribution "Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency" awarded


Free publishing in Springer Nature Open Access journals for members of Leibniz Universität Hannover


Prof. Dr. Ralph Ewerth is Fellow of the research group "Multimodal Rhetoric in Online Media Communications"


TIB presents various AI projects at "Speed Dating"


Best Paper Award for the paper "Understanding, Categorizing and Predicting Semantic Image-Text Relations"


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