Academic Profile

I do research in legal informatics, legal data analytics, and natural language processing of legal text.

UPDATE / MOVE TO TUM: In January 2021 I joined the Department of Informatics at the Technical University of Munich as a tenure-track Assistant Professor in LegalTech. I am still mentoring MCDS Capstone Projects at CMU LTI as adjunct faculty. An overhauled website at TUM will replace this one soon.

My work is best described as (Legal) Knowledge Engineering or (Legal) Data Science. It draws from artificial intelligence & law, knowledge representation & reasoning, natural language processing, applied machine learning, information retrieval as well as computational models of argument.

I obtained a diploma in law from the University of Augsburg, Germany, as well as a Master of Laws (LLM) and PhD in Intelligent Systems under Prof. Kevin Ashley at the University of Pittsburgh. I went on to work for Prof. Eric Nyberg at CMU LTI from 2015 to 2019 as a Visiting Researcher, Postdoc, and eventually Systems Scientist. After a year at SINC as a Legal Data Scientist I joined the informatics department at TUM.

Links: Google Scholar Profile

Research & Teaching

  • The LUIMA project in automated extraction of semantic information from legal texts towards a retrieval of legal documents that seamlessly connects to a lawyer’s understanding of her/his problem. It involves project collaboration with the AI&Law group at the University of Pittsburgh’s LRDC and the LLT Lab at Hofstra University School of Law.
  • I have been co-teaching AI&Law Tutorials at a number of conferences (Jurix 2011, ICAIL 2013, 2015, 2017) and at the Heidelberg School of Law Graduate Collegium in Digital Law. I also am a co-organizer of the ASAIL workshop series (2015, 2017, 2019, 2020).
  • The VJAP model of value-based formal models of legal argumentation and case outcome prediction, which is my dissertation topic.
  • I co-taught core courses in CMU LTI’s Master of Computational Data Science (MCDS) Program and mentor student project work.


I was involved in the Jessup Moot Court Competition for a number of years and taught Public International Law Advocacy at the University of Pittsburgh School of Law.


  • 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 Final Version at 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.
  • 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.
  • 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.
  • J. Savelka, V.R. Walker, M. Grabmair and K.D. Ashley, Sentence Boundary Detection in Adjudicatory Decisions in the United States, TAL 58.2, 2017.
  • 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.
  • M. Grabmair, Predicting trade secret case outcomes using argument schemes and learned quantitative value effect tradeoffs, Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law (ICAIL 2017), 89-98, ACM, 2017, PDF preprint ACM DL Author-ize serviceFinal Version at 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).
  • M. Grabmair, K.D. Ashley, R. Chen, P. Sureshkumar, C. Wang, E. Nyberg, V.R. Walker, Introducing LUIMA: an experiment in legal conceptual retrieval of vaccine injury decisions using a UIMA type system and tools, Proceedings of the 15th International Conference on Artificial Intelligence and Law (ICAIL 2015), 69-78, ACM, 2015, PDF preprint ACM DL Author-ize serviceOfficial version at 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). Amsterdam: IOS Press, 2014, pp. 133-142.
  • P. M. Sweeney, E. F. Bjerke, M. A. Potter, H. Güçlü, 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.) (2014). 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, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (ICAIL 2013), 73-82, ACM, 2013, PDF preprint ACM DL Author-ize serviceFinal Version at 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, Proceedings of the 13th International Conference on Artificial Intelligence and Law (ICAIL 2011), 161-170, ACM, 2011 [Donald H. Berman Best Student Paper Award] PDF preprint ACM DL Author-ize serviceFinal Version at 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.
  • 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).