Gjergji Kasneci
Gjergji Kasneci is a professor of Responsible Data Science at TU Munich, where his research covers transparency, robustness, bias and fairness in machine learning. He co-wrote the 2007 YAGO paper with Fabian Suchanek and Gerhard Weikum, and the Seoul Test of Time Award recognized that work in 2018. His 2009 doctorate at Saarland University focused on semantic search. He led a web mining group at the Hasso Plattner Institute from 2011 to 2014, then served as chief technology officer at SCHUFA Holding AG from 2017 to 2022. From 2018 to 2023 he also led a data science group at the University of Tübingen as an honorary professor.
- Works
- 237
- Citations
- 17,435
- h-index
- 33
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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2005–2009
A PhD in semantic search
He completes an MSc in computer science and mathematics at Marburg in 2005, then earns a PhD at Saarland University in 2009, specializing in semantic search.
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2007
The YAGO knowledge base
In 2007 he co-writes the YAGO paper with Fabian Suchanek and Gerhard Weikum.
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2009
A postdoc in Cambridge
In 2009 he joins Microsoft Research Cambridge as a postdoctoral researcher, working on probabilistic inference over knowledge bases.
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2011–2014
A web mining research group
From 2011 to 2014 he leads the Web Mining Research group at the Hasso Plattner Institute as a senior researcher.
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2014–2022
Chief technology officer at SCHUFA
He joins SCHUFA Holding AG in 2014 and serves as its chief technology officer from 2017 to 2022.
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2018
The Seoul Test of Time Award
In 2018 the International World Wide Web Conference Committee gives him the Seoul Test of Time Award for developing YAGO.
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2018–2023
Honorary professor at Tübingen
From 2018 to 2023 he leads the Data Science and Analytics group at the University of Tübingen as an honorary professor, focusing on explainable, robust and scalable AI.
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Since 2023
Chair of Responsible Data Science
In 2023 he takes up a professorship in Responsible Data Science at TU Munich, where his research covers transparency, robustness, bias and fairness in machine learning.
Sources and credits
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