Department of Computer Science

Dr. Gjergji Kasneci

After completing my PhD in Graph-based Mining and Retrieval at the Max Planck Institute for Computer Science in Saarbrücken, Germany, in 2009, I joined Microsoft Research in Cambridge, UK, as a postdoctoral researcher, where I worked on probabilistic inference in knowledge bases. In 2011, I joined the Hasso Plattner Institute in Potsdam, Germany, where I led the Web Mining and Analytics Research Group. Mid 2014, I joined SCHUFA Holding AG, where I currently hold the CTO position. Since April 2018, I am also leading the Data Science and Analytics Research Group at the University of Tübingen.

Research Interests

  • Feature engineering: The extraction and development of meaningful and explainable features
  • Predictive modelling: The development of robust and explainable predictive models
  • Quality assessment of training data sets: The development of techniques for quantifying the quality of training data



Training decision trees as replacement for convolution layers (2020) by Wolfgang Fuhl, Gjergji Kasneci, Wolfgang Rosenstiel and Enkelejda Kasneci was accepted at AAAI 2020 and is currently published on arXiv (Abstract, .bib-file).


Towards User Empowerment (2019)by Martin Pawelczyk, Johannes Haug, Klaus Broelemann and Gjergji Kasneci was accepted at the NeurIPS workshop on Human-Centric Machine Learning and was published at arXiv (Abstract, .bib-file).

CancelOut: A Layer for Feature Selection in Deep Neural Networks (2019) by Vadim Borisov, Johannes Haug and Gjergji Kasneci was published at ICANN 2019 (Abstract, .bib-file).

A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees (2019) by Klaus Broelemann and Gjergji Kasneci was published at IJCAI 2019 (Abstract, .bib-file).

Validation Loss for Landmark Detection (2019) by Wolfgang Fuhl, Thomas Kübler, Rene Alexander Lotz, Gjergji Kasneci, Wolfgang Rosenstiel and Enkelejda Kasneci was published as a preprint on arXiv (Abstract, .bib-file).