Skip area navigation

Dr. Timothy Darr

Assistant Professor of Computer Science

Office Location Bailey Business Center, 238
Phone (Office) 405.585.4417
OBU Mailbox # 61737
Email timothy.darr@okbu.edu
See full CV.

Biography

Dr. Timothy P. Darr joined OBU with more then twenty years experience in the computer science and computer information systems industry. He is an artificial Intelligence (AI) technology researcher and practitioner with particular focus on knowledge representation, human decision modeling, and intelligent agent modeling. He has practical experience in applying a multitude of cutting-edge technologies to real-world problems, including: natural language processing and understanding, data mining, machine learning, deep learning with neural networks, sentiment and opinion mining, social network analysis, and social media analytics. He has working familiarity with a variety of programming languages (C, Java, Python, Clojure, Visual Basic, Prolog, Lisp), data and knowledge models (RDF/OWL, JSON, XML/XSL), query languages (SQL, SPARQL), programming methods (object-oriented, functional), and software project management methods (waterfall, agile, scrum, X programming). Dr. Darr’s publications have appeared in refereed journals and conferences. His research has appeared in invited workshops. He has co-authored chapters in books on concurrent engineering and expert systems.

Dr. Darr has successfully led small development teams delivering customer success in the government and commercial sectors. In the government sector, he led agile teams in transitioning applied research solutions to the Navy, Air Force, Army and intelligence community. In the commercial sector, he led teams in building and customizing knowledge bases for enterprise-scale applications in the computer and telecommunications industries for Fortune 500 companies.

Biography

Dr. Timothy P. Darr joined OBU with more then twenty years experience in the computer science and computer information systems industry. He is an artificial Intelligence (AI) technology researcher and practitioner with particular focus on knowledge representation, human decision modeling, and intelligent agent modeling. He has practical experience in applying a multitude of cutting-edge technologies to real-world problems, including: natural language processing and understanding, data mining, machine learning, deep learning with neural networks, sentiment and opinion mining, social network analysis, and social media analytics. He has working familiarity with a variety of programming languages (C, Java, Python, Clojure, Visual Basic, Prolog, Lisp), data and knowledge models (RDF/OWL, JSON, XML/XSL), query languages (SQL, SPARQL), programming methods (object-oriented, functional), and software project management methods (waterfall, agile, scrum, X programming). Dr. Darr’s publications have appeared in refereed journals and conferences. His research has appeared in invited workshops. He has co-authored chapters in books on concurrent engineering and expert systems.

Dr. Darr has successfully led small development teams delivering customer success in the government and commercial sectors. In the government sector, he led agile teams in transitioning applied research solutions to the Navy, Air Force, Army and intelligence community. In the commercial sector, he led teams in building and customizing knowledge bases for enterprise-scale applications in the computer and telecommunications industries for Fortune 500 companies.

Education

  • Ph. D. Computer Science and Engineering (May 1997) - The University of Michigan
  • M. S. E. Computer Science and Engineering (May 1992) - The University of Michigan
  • B. S. E. Computer Engineering Summa Cum Laude (December 1988) - The University of Michigan

Subjects Taught

Introductory computer science (programming), network modeling, anomaly detection.

Professional Memberships

  • AAAI
  • IEEE
  • ACM

Selected Publications

  • Hancock, B. J., Darr, T. P., Hazell, R., Grazaitis, P., "Integrating Civil Affairs Through the Application of Battlefield Relevant Civil Information Management," Civil Affairs Association Issue Papers (to appear), 2019.
  • Darr, T. P., Mayer, R., Jones, R. D., Ramey, T., Smith, R. and Zimmerman, C., "Quantum Probability Models for Decision Making," in 24th International Command and Control Research & Technology Symposium, Laurel, MD, 2019.
  • Houser, D., Wang, J., Darr, T. P. & Mayer, R., (2019). Time pressure improves decisions in generalized Colonel Blotto games. 24th International Command and Control Research & Technology Symposium. Laurel, MD
  • Darr, T.P., Benjamin, P. and Mayer, R., “A Utility-Theoretic Preference Model for OPFOR Course-of-Action Selection and Assessment”, HSCB Focus 2011: Integrating Social Science Theory and Analytic Methods for Operational Use, February 8-10, 2011.
  • Darr, T. P. and Birmingham, W. P., “Part-selection Triptych: A Representation, Problem Properties and Problem Definition, and Problem-solving Method”, AI EDAM, Vol. 14, No. 1, 2000, pp. 39-52.