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.
- 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
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms by Martin Pawelczyk, Sascha Bielawski, Johan Van den Heuvel, Tobias Richter and Gjergji Kasneci was accepted at NeurIPS 2021 (Datasets and Benchmarks Track) (github).
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types by Wolfgang Fuhl, Gjergji Kasneci and Enkelejda Kasneci was accepted at ISMAR 2021 (dataset).
"TüEyeQ, a rich IQ test performance data set with eye movement, educational and socio-demographic information" by Enkelejda Kasneci, Gjergji Kasneci, Tobias Appel, Johannes Haug, Franz Wortha, Maike Tibus, Ulrich Trautwein and Peter Gerjets was accepted at the Nature Scientific Data Journal (8, 2021) (dataset, github)
On Baselines for Local Feature Attributions by Johannes Haug, Stefan Zürn, Peter El-Jiz and Gjergji Kasneci was accepted at the Workshop on Explainably Agency in AI at AAAI 2021 (Abstract, .bib-file, github)
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection by Johannes Haug, Martin Pawelczyk, Klaus Broelemann and Gjergji Kasneci was accepted at KDD 2020 (Abstract, .bib-file, github).
On Learning Invariant Counterfactual Explanations under Predictive Multiplicity by Martin Pawelczyk, Klaus Broelemann and Gjergji Kasneci was accepted at UAI 2020 (Abstract, .bib-file).
PLAY: A Profiled Linear Weighting Scheme for Understanding the Influence of Input Variables on the Output of a Deep Artificial Neural Network by Torsten Dietl, Gjergji Kasneci, Johannes Fürnkranz and Eneldo Loza Mencía was accepted at the Archives of Data Science, Series A (Abstract, .bib-file).
Learning Model Agnostic Counterfactual Explanations for Tabular Data by Martin Pawelczyk, Klaus Broelemann and Gjergji Kasneci was accepted at WWW 2020 (Abstract, .bib-file).
Bias in Data-driven AI Systems - An Introductory Survey by Ntoutsi et al. was accepted at WIRE's Data Mining and Knowledge Discovery Journal. This is the first multi-disciplinary publication from our EU "NoBias" Project (Abstract, .bib-file).
Training Decision Trees as Replacement for Convolution Layers 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 2019 workshop on Human-Centric Machine Learning and was published at arXiv (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).