Satoshi Tojo

Professor of Information Science, Computational Linguistics, Computational Music
Japan Advanced Institute of Science and Technology, Ishikawa, Japan

Prof. Tojo received degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering from University of Tokyo, Japan. He joined Mitsubishi Research Institute, Inc. (MRI) in 1983, and Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan, as associate professor in 1995; professor from 2000.

His research interest includes formal semantics of natural language, logic in artificial intelligence (knowledge and belief of rational agents), grammar acquisition, and the novel field of computational music (linguistic models of music).


Mihai Surdeanu

Associate Professor of Computer Science
University of Arizona, USA

Mihai Surdeanu is an Associate Professor in the Computer Science department at University of Arizona.

Dr. Surdeanu earned a PhD degree in Computer Science from Southern Methodist University, Dallas, TX, in 2001. He has 15+ years of experience in building systems driven by natural language processing (NLP) and machine learning. His experience spans both academia (Stanford University, University of Arizona) and industry (Yahoo! Research and two NLP-centric startups).

During his career he published more than 80 peer-reviewed articles, including two articles that were among the top three most cited articles at two different NLP conferences. He was leader or member of teams that ranked in the top three at seven highly competitive international evaluations of end-user NLP systems such as question answering and information extraction. His work was funded by several government organizations (DARPA, NIH), as well as private foundations (the Allen Institute for Artificial Intelligence, the Bill & Melinda Gates Foundation).

Dr. Surdeanu’s current work focuses on using machine reading to extract structure from free text, and using this structure to construct causal models that can be used to understand, explain, and predict hypotheses for precision medicine.