The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)

Julia (Taylor) Rayz


 Julia (Taylor) Rayz


Professor and Associate Department Head 




Ph.D. Computer Science & Engineering, University of Cincinnati, 2008 

Prior Appointments

Senior Research Engineer, Ontological Semantics, RiverGlass, Inc.
Research Assistant Professor, CERIAS 

Research Areas

Natural Language Understanding, Knowledge Discovery and Representation, Computational Recognition of Salient Information in Texts, Uncertainty Management 

Key Areas

Computational Linguistics, Ontological Semantics, Knowledge Representation 

Notable Awards

CIT Outstanding Faculty in Discovery, 2015

ICCI*CC Best Paper award, 2014

CERIAS Fellow, 2012

NAFIPS Best Student Paper finalist, 2008

ISHS Emerging Scholar Award, 2004 

Notable Affiliations

Association for the Advancement of Artificial Intelligence (AAAI)
Association for Computing Machinery (ACM)
Cognitive Science Society (CogSci)
Institute of Electrical and Electronic Engineers (IEEE)
Institute of Electrical and Electronic Engineers Computational Intelligence Society
International Society for Humor Studies (ISHS)
Society for Design and Process Science (SDPS), applied 


My primary research interests lie in natural language understanding, knowledge discovery and representation, and computational recognition of salient information in texts, as well as in uncertainty management. These belong in the domain of Artificial Intelligence, in the areas of Natural Language Processing, and Cognitive Science on the one hand, and Imprecision Management on the other.

I chose computational detection of humor for my 2008 dissertation, as humor was a perfect test platform for multiple meaning detection in the same natural language text.

My long-term research interest and goal are to enable people to communicate with computers informally, using (eventually, any) natural language, with the full understanding of what is said, and perhaps what is more important, of what is left unsaid. While knowledge representation, reasoning, machine learning and computational linguistics are not new areas and have received considerable attention, combining the meaning extracted from natural language texts with our knowledge of the world, represented in some conceptual form, with built-in fuzziness, vagueness, and uncertainty, where necessary, the way people do in real life -- this computational task has not been resolved. My current research attempts to come closer to an understanding of how to construct such a model, and modeling and detecting humor has turned out to be a convenient and visible entry into it as well as a good testing mechanism. Such representation of text provides an opportunity to convert unstructured data into a more structured form (similar to graphs, lattices or hierarchies), and ultimately, to the fully structured databases, without losing information contained in it.