Prof. Matthias Grabmair, Ph.D., LL.M.

Matthias Grabmair is a tenure-track Assistant Professor of Legal Tech in the Department of Informatics at the Technical University of Munich. Before joining TUM in January 2021, he worked as a Legal Data Scientist at the German legal informatics company SINC (2019-2020). Prior to that, he spent four years at Carnegie Mellon University's Language Technologies Institute working with Prof. Eric Nyberg as a Visiting Researcher, Postdoc, and Systems Scientist (2015-2019). He obtained a diploma in law from the University of Augsburg, Germany, as well as a Master of Laws (LL.M.) and Ph.D. in Intelligent Systems mentored by Prof. Kevin Ashley from the University of Pittsburgh.

Dr. Grabmair serves as the section editor for Machine Learning of the Journal of Artificial Intelligence & Law and co-founded the ASAIL Workshop Series on Automated Extraction of Semantic Information in Legal Text, for which he has also been chairing the organizing committee since 2019.


CV [2/2021]  

Google Scholar



  • J. Savelka, M. Grabmair, K. Ashley, A Law School Course in Applied Legal Analytics and AI, Law in Context. A Socio-legal Journal 37, no. 1 (2020): 1-41. [Open Access]
  • A. Belova, M. Grabmair, E. Nyberg, Segmentation of Rulemaking Documents for Public Notice- and-Comment Process Analysis, Proceedings of the Workshop on Artificial Intelligence and the Administrative State (AIAS 2019),,, 2019.
  • L. Zhong, Z. Zhong, Z. Zhao, S. Wang, K. D. Ashley, and M. Grabmair, Automatic Summarization of Legal Decisions using Iterative Masking of Predictive Sentences, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (ICAIL 2019), 163-172, ACM, 2019. [PDF @ ACM]
  • B. Karki, F. Hu, N. Haridas, S. Barot, Z. Liu, L. Callebert, M. Grabmair, and A. Tomasic, Question answering via web extracted tables, Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM 2019), Article 4, 8 pages, ACM, 2019. [PDF @ ACM]
  • M. Kale, A. Siddhant, S. Nag, R. Parik, M. Grabmair, A. Tomasic, Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks, 2nd Learning from Limited Labeled Data (LLD) Workshop at ICLR 2019, 2019. [arxiv]
  • S. Wadhwa, V. Embar, M. Grabmair, E. Nyberg, Towards Inference-Oriented Reading Comprehension: ParallelQA, Workshop on New Forms of Generalization in Deep Learning and Natural Language Processing, NAACL 2018, arXiv:1805.03830, 2018. [arxiv]
  • J. Savelka, Vern R. Walker, M. Grabmair and K. D. Ashley, Sentence Boundary Detection in Adjudicatory Decisions in the United States, TAL 58.2, 2017. [PDF]
  • A. Ravichander, T. Manzini, M. Grabmair, G. Neubig, J. Francis and E. Nyberg, How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing, Proceedings SIGDIAL 2017, pp 374-383, ACL, 2017. [PDF]
  • M. Grabmair, Predicting Trade Secret Case Outcomes using Argument Schemes and Learned Quantitative Value Effect Tradeoffs, ICAIL 2017 Proceedings, ACM, 2017. [preprint] [PDF @ ACM]
  • A. Bansal, Z. Bu, B. Mishra, S. Wang, K. D. Ashley and M. Grabmair, Document Ranking with Citation Information and Oversampling Sentence Classification in the LUIMA Framework, Proceedings of the 2016 International Conference on Legal Knowledge and Information Systems (JURIX 2016), pp 33-42, IOS Press, 2016.
  • M. Grabmair, Modeling Purposive Legal Argumentation and Case Outcome Prediction using Argument Schemes in the Value Judgment Formalism. Doctoral Dissertation, University of Pittsburgh, 2016. [PDF @ Pitt]
  • M. Grabmair, K. D. Ashley, R. Chen, P. Sureshkumar, C. Wang, E. Nyberg and V. R. Walker, Introducing LUIMA: An Experiment in Legal Conceptual Retrieval of Vaccine Injury Decisions using a UIMA Type System and Tools, ICAIL 2015 Proceedings, 69-78, ACM, 2015. [preprint] [PDF @ ACM]
  • J. Savelka, M. Grabmair, K.D. Ashley, Mining Information from Statutory Texts in Multi-jurisdictional Settings. In Rinke Hoekstra. Legal Knowledge and Information Systems (JURIX 2014). Amster- dam: IOS Press, pp. 133-142, 2014.
  • P. M. Sweeney, E. F. Bjerke, M. A. Potter, H. Güçlu, C. R. Keane, K. D. Ashley, M. Grabmair, R. Hwa, Network Analysis of Manually-Encoded State Laws and Prospects for Automation. In Winkels, R.; Lettieri, N.; Faro, S. (Eds.). Network Analysis in Law. Collana: Diritto Scienza Tecnologia/Law Science Technology Temi, 3, Napoli: Edizioni Scientifiche Italiane, 2014.
  • M. Grabmair & K.D. Ashley, Using Event Progression to Enhance Purposive Argumentation in the Value Judgment Formalism, ICAIL 2013 Proceedings, 73-82, ACM, 2013. [preprint] [PDF @ ACM]
  • M. Grabmair & K.D. Ashley, A Survey of Uncertainties and their Consequences in Probabilistic Legal Argumentation, in: Bayesian Argumentation - The Practical Side of Probability (Frank Zenker ed.), S. 61-85, Springer, 2012.
  • M. Grabmair, K.D. Ashley, R. Hwa and P.M. Sweeney, Toward Extracting Information from Public Health Statutes using Text Classification and Machine Learning, Jurix 2011: The 24th Annual Conference, pp. 73-82 (Katie M. Atkinson ed.) IOS Press, 2011.
  • M. Grabmair & K.D. Ashley, Facilitating Case Comparison Using Value Judgments and Intermediate Legal Concepts, ICAIL 2011 Proceedings, 161-170, ACM, 2011. [Donald H. Berman Best Student Paper Award] [preprint] [PDF @ ACM]
  • M. Grabmair & K.D. Ashley, Argumentation with Value Judgments - An Example of Hypothetical Reasoning, Jurix 2010: The 23rd Annual Conference, 67-76 (R.G.F. Winkels ed.), IOS Press, 2010.
  • M. Grabmair, T.F. Gordon, and D. Walton, Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks, Proceedings of the Third International Conference on Computational Models of Argument (COMMA), 255-266, IOS Press, 2010. [PDF]
  • M. Grabmair & K.D. Ashley, Using Critical Questions to Disambiguate and Formalize Statutory Provisions, ICAIL 2009 Proceedings, 240-241, ACM SIGART, 2009.
  • M. Grabmair & K.D. Ashley, Towards Modeling Systematic Interpretation of Codified Law, Jurix 2005: The Eighteenth Annual Conference, 107-108 (M.-F. Moens, P. Spyns ed.), IOS Press, 2005.