Academic Profile

I am a postdoctoral associate at CMU LTI working with Prof. Eric Nyberg on solving problems in intelligent legal information management and intelligent natural language dialogue systems while also teaching at the institute.

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.


  • 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 close collaboration with the AI&Law group at the University of Pittsburgh’s LRDC and the LLT Lab at Hofstra University School of Law.
  • The DialogQA project in developing a cognitive agent system able to engage in a question answering dialogue with a user, thereby maintain the conversational and topical context, as well as proactively obtain new information during the dialogue.
  • The VJAP model of value-based formal models of legal argumentation and case outcome prediction, which is my dissertation topic.
  • I co-teach core courses in CMU LTI’s Master of Computational Data Science (MCDS) Program and mentor student project work.


I have been 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. This influences my research and teaching. I emphasize the ability of students to communicate their work and ideas effectively and engage in a productive dialogue with their peers and domain expert collaborators.

I spend a large part of my time with research-focused programming. During my exposure to symbolic AI, I acquired a taste for functional programming and LISP. My daily development toolchain largely consists of Clojure (-script), Vim, Eclipse, Java, UIMA RUTA, Python and the fountain of libraries coming out of Github.


  • M. Grabmair, Predicting Trade Secret Case Outcomes using Argument Schemes and Learned Quantitative Value Effect Tradeoffs, Proceedings ICAIL 2017 (IN PRINT).
  • 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 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.
  • 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, ICAIL 2013 Proceedings, 73-82, ACM, 2013. pdf
  • 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]
  • 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).