Mathias Niepert

Professor, University of Stuttgart
Universit├Ątsstra├če 32
70569 Stuttgart
mathias dot niepert at simtech dot uni-stuttgart dot de

Chief Scientific Advisor, NEC Labs Europe
mathias dot niepert at neclab dot eu

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Mathias is a full professor (W3) at the University of Stuttgart and a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS). He heads the Machine Learning and Simulation Science Lab. His professorship is part of the Cluster of Excellence for the Simulation Sciences (SimTech) and the Department of Computer Science. He is also a Chief Scientific Advisor at NEC Laboratories Europe. At NEC Labs Europe he was senior (2015-2017) and chief research scientist (2017-2021) as well as manager (2019-2021) of the machine learning group. From 2013-2015 he was a postdoctoral research associate at the Allen School of Computer Science, University of Washington. Mathias was also a member of the Data and Web Science Research Group at the University of Mannheim. Mathias obtained his PhD from Indiana University in computer science with a minor in scientific computing.

His group's research interests include representation learning for graph-structured data, geometric deep learning, probabilistic graphical models, and the intersection of ML and the simulation sciences. His group's methods are concerned with learning, inducing, and leveraging relational structure with applications in vision, natural language processing, and the (bio-)medical domain. Mathias is also co-founder of several open-source digital humanities projects such as the Indiana Philosophy Ontology Project.

If you are interested in his group's work (published regularly at ICML, NeurIPS, AAAI, EMNLP, IJCAI, ICLR) please take a look at the publication section of this website and visit our video channel.

There are several PhD and postdoctoral positions avilable in the MLSim group at the University of Stuttgart.



  • Two long papers and one demo paper accepted to ACL 2022.
  • Best paper award at the International Conference on Automated Knowledge Base Construction (AKBC) 2021.
  • Two papers accepted at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
  • A paper on using behavioral analysis from software engineering for knowledge graph embedding methods accepted at AKBC 2021.
  • A paper on profiling users from the perspective of network operators accepted at CoNext 2021.
  • The paper "VEGN: Variant Effect Prediction with Graph Neural Networks" was accepted at the ICML Workshop on Computational Biology (WCB).
  • Best reviewer award at the International Conference on Uncertainty in Artificial Intelligence (UAI) 2021.
  • Best reviewer award at the International Conference on Learning Representations (ICLR) 2021.
  • The paper "Uncertainty Quantification and Calibration with Finite-State Probabilistic RNNs" was accepted at the International Conference on Learning Representations (ICLR) 2021.
  • The machine learning group has three of its papers accepted at the Conference on Artificial Intelligence (AAAI) 2021.
  • One paper accepted to the European Conference on Information Retrieval (ECIR) 2020.
  • Contributed several chapters to the book Introduction to Lifted Probabilistic Inference from MIT press.
  • Invited talks at Twitter research, SMiLE workshop, Multi-Modal Knowledge graphs @ AKBC.
  • Two papers accepted at EMNLP 2019: Attending to Future Tokens For Bidirectional Sequence Generation and Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas
  • Two papers accepted at ICML 2019: State-Regularized Recurrent Neural Networks and Learning Discrete Structures for Graph Neural Networks
  • One paper accepted to IJCAI 2019: A comparative study of distributional and symbolic paradigms for relational learning
  • I am an invited speaker at the IPAM workshop "Geometric Deep Learning for Big Data and Applications" in LA, the Karlsruhe.AI and Heidelberg.AI speaker series, the RIKEN AI Insitute in Tokyo, and the ECML workshop "New Trends in Representation Learning with Knowledge Graphs"