Denis Krompaß

Currently, I am a Research Scientist at Siemens Corporate Technology.
I received my PhD in Computer Science from the Ludwig-Maximilian-University of Munich in 2015. My supervisor was Prof. Dr. Volker Tresp. Before that, I studied computational biology at the LMU/TU Munich and received my Master's degree in 2012.

Contact

Interests

›Machine Learning, Data Analytics
›Deep Learning
›Representation Learning
›Latent Variable Models
›Matrix/Tensor Factorization
›Knowledge Graphs
›Probabilisitic Databases
›Recomendation Systems
›Link Prediction
›Machine Learning for Precision Medicine
›Bioinformatics

Publications

2015

›Denis Krompaß, Stephan Baier, and Volker Tresp. Type-Constrained Representation Learning in Knowledge Graphs. Proceedings of the International Semantic Web Conference (ISWC), 2015. [PDF] [BIBTEX] [Code and Datasets]
›Denis Krompaß and Volker Tresp. Ensemble Solutions for Link-Prediction in Knowledge Graphs. Proceedings of the 2nd ECML workshop on Linked Data for Knowledge Discovery, 2015. [PDF] [BIBTEX]
›Denis Krompaß, Christóbal Esteban, Volker Tresp, Martin Sedlmayr and Thomas Ganslandt. Exploiting Latent Embeddings of Nominal Clinical Data for Predicting Hospital Readmission. KI - Künstliche Intelligenz, December 2014. [PDF] [BIBTEX]

2014

›Denis Krompaß, Maximilian Nickel, and Volker Tresp. Large-Scale Factorization of Type-Constrained Multi-Relational Data. International Conference on Data Science and Advanced Analytics (DSAA’2014), 2014. [PDF] [BIBTEX] [Slides]
›Denis Krompaß, Maximilian Nickel, and Volker Tresp. Querying Factorized Probabilistic Triple Databases. Proceedings of the International Semantic Web Conference (ISWC), 2014. (Best Research Paper nominee) [PDF] [BIBTEX] [Slides] [Video]
›Denis Krompaß, Xueyan Jiang, Maximilian Nickel, and Volker Tresp. Probabilistic Latent-Factor Database Models. Proceedings of the ECML workshop on Linked Data for Knowledge Discovery, 2014. [PDF] [BIBTEX] [Slides]

2013

›Volker Tresp, Sonja Zillner, Maria J. Costa, Yi Huang, Alexander Cavallaro, Peter A. Fasching, Andre Reis, MartinSedlmayr, Thomas Ganslandt, Klemens Budde, Carl Hinrichs, Danilo Schmidt, Philipp Daumke, Daniel Sonntag, Thomas Wittenberg, Patricia G. Oppelt, and Denis Krompass. Towards a New Science of a Clinical Data Intelligence. NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and Healthcare, CoRR, arXiv:1311.4180 [cs.CY],
›Denis Krompaß, Maximilian Nickel, Xueyan Jiang, and Volker Tresp. Non-Negative Tensor Factorization with RESCAL. ECML/PKDD 2013 Workshop on Tensor Methods for Machine Learning, 2013. [PDF] [Slides] [Code]
›Xueyan Jiang, Volker Tresp and Denis Krompass. A Logistic Additive Model for Relation Prediction in Multi-relational data. ECML/PKDD 2013 Workshop on Tensor Methods for Machine Learning, 2013.

2012

›Predrag Radivojac et al. A large-scale evaluation of computational protein function prediction. Nature Methods 10, 221-227, 2013
›Andre J. Aberer, Denis Krompass, Alexandros Stamatakis. Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice. In Systematic Biology, 62(1):162-166, 2012.

2011

›Simon A. Berger, Denis Krompass, Alexandros Stamatakis. Performance, Accuracy and Web-Server for Evolutionary Placement of Short Sequence Reads under maximum-likelihood. In Systematic Biology, 60(3):291-302, 2011.
›Tobias Hamp et al. Homology-based inference sets the bar high for protein function prediction. BMC Bioinformatics (CAFA 2011), 14 Suppl 3, S7.