Professor Kevin Ashley is a faculty member of the Graduate Program in Intelligent Systems at the University of Pittsburgh, a Senior Scientist at the Learning Research and Development Center, a Professor of Law, and Adjunct Professor of Computer Science. He received a B.A. in philosophy (magna cum laude) from Princeton University in 1973, J.D. (cum laude) from Harvard Law School in 1976, and Ph.D. in computer science in 1988 from the University of Massachusetts. He is the author of Modeling Legal Argument: Reasoning with Cases and Hypotheticals (MIT Press/Bradford Books, 1990) and of Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age (Cambridge University Press, 2017).

Matthias Grabmair is a Systems Scientist at Carnegie Mellon University’s Language Technologies Institute. He teaches in a Computational Data Science Masters Program and conducts research on natural language processing of legal text, machine learning on legal data, and computational models of legal argumentation. He has been active in the AI&Law community since 2005, regularly serves as a reviewer for relevant conferences, and is a member of the editorial board of the AI&Law Journal. He is formally trained in both law and AI, holding a Diploma in Law from the University of Augsburg, Germany, as well as an LLM and a PhD in Intelligent System from the University of Pittsburgh.

Jaromir Savelka focuses on semantic processing of unstructured legal data. In his proposed dissertation he works on discovering of sentences for argumentation about meaning of statutory and regulatory terms (proposal). He has extensive experience with text categorization and transformation of unstructured data into a structured representation (network, relational database). He has worked on knowledge re-use techniques for automatic classification of legal texts applying concepts from transfer learning. He has also experimented with an interactive machine learning to support legal text analysis. During his studies he focused on applications of machine learning and natural language processing to legal domain. He has a law degree and fairly good understanding of European copyright, IT Law, and legal practice in general.

Prior to joining the Hofstra faculty, Professor Vern Walker was a partner in the Washington, D.C., law firm of Swidler & Berlin. His practice included representation before state and federal administrative agencies and before courts on judicial review of agency actions. His administrative practice focused primarily on issues concerning public health, safety, and the environment. He also represented clients in civil litigation alleging products liability and toxic torts. Professor Walker has a doctorate in philosophy, with specialization in knowledge theory, artificial intelligence, deductive and inductive logic, and the conceptual foundations and methodologies of the sciences. His doctoral dissertation was on the perception of objects by biological and mechanical systems. While in law practice, he worked extensively with expert witnesses and scientific evidence, and he co-authored the book Product Risk Reduction in the Chemical Industry. Since joining Hofstra, he has published extensively on the logic of legal reasoning and factfinding, on the design of factfinding processes, and on the use of scientific evidence in legal proceedings. His writings explore the substantive topics of risk assessment, risk management, and scientific uncertainty.

“Alumni” Student Contributors

CMU Master of Computational Data Science:

  • Apoorva Bansal
  • Zheyuan Bu
  • Yu-Ju Chang
  • Ran Chen
  • Maria Johnson
  • Yichi Liu
  • Biswajeet Mishra
  • Sheng Qian
  • Preethi Sureshkumar
  • Chen Wang
  • Silun Wang
  • Meng Wei
  • Bingqing Wu
  • Che Zheng
  • Qiang Zhu